From 9179bad0ae25b7bfd0ed27a24d3e170c694378d3 Mon Sep 17 00:00:00 2001
From: Corentin Le Molgat <corentinl@google.com>
Date: Thu, 13 Jul 2023 14:34:43 +0200
Subject: [PATCH] examples: Bump notebooks

---
 examples/notebook/algorithms/knapsack.ipynb   |   46 +-
 .../algorithms/simple_knapsack_program.ipynb  |   24 +-
 .../constraint_solver/cp_is_fun_cp.ipynb      |   47 +-
 .../constraint_solver/cvrptw_break.ipynb      |  248 +-
 .../constraint_solver/nqueens_cp.ipynb        |   23 +-
 .../constraint_solver/simple_cp_program.ipynb |   27 +-
 .../simple_routing_program.ipynb              |   14 +-
 examples/notebook/constraint_solver/tsp.ipynb |   57 +-
 .../constraint_solver/tsp_circuit_board.ipynb |  130 +-
 .../constraint_solver/tsp_cities.ipynb        |   28 +-
 .../tsp_distance_matrix.ipynb                 |  114 +-
 examples/notebook/constraint_solver/vrp.ipynb |  123 +-
 .../constraint_solver/vrp_breaks.ipynb        |   72 +-
 .../constraint_solver/vrp_capacity.ipynb      |  145 +-
 .../constraint_solver/vrp_drop_nodes.ipynb    |  151 +-
 .../constraint_solver/vrp_global_span.ipynb   |  128 +-
 .../vrp_initial_routes.ipynb                  |  135 +-
 .../constraint_solver/vrp_node_max.ipynb      |  171 +-
 .../vrp_pickup_delivery.ipynb                 |  138 +-
 .../vrp_pickup_delivery_fifo.ipynb            |  141 +-
 .../vrp_pickup_delivery_lifo.ipynb            |  141 +-
 .../constraint_solver/vrp_resources.ipynb     |   89 +-
 .../vrp_solution_callback.ipynb               |  149 +-
 .../constraint_solver/vrp_starts_ends.ipynb   |  129 +-
 .../constraint_solver/vrp_time_windows.ipynb  |   68 +-
 .../constraint_solver/vrp_tokens.ipynb        |   61 +-
 .../vrp_with_time_limit.ipynb                 |   26 +-
 .../notebook/constraint_solver/vrpgs.ipynb    |   83 +-
 .../vrptw_store_solution_data.ipynb           |   71 +-
 examples/notebook/examples/appointments.ipynb |  193 +-
 .../assignment_with_constraints_sat.ipynb     |   69 +-
 .../notebook/examples/balance_group_sat.ipynb |   71 +-
 .../examples/bus_driver_scheduling_sat.ipynb  | 3447 ++++++++---------
 .../examples/chemical_balance_sat.ipynb       |   28 +-
 .../notebook/examples/clustering_sat.ipynb    |   30 +-
 .../examples/cover_rectangle_sat.ipynb        |   45 +-
 .../notebook/examples/cryptarithm_sat.ipynb   |   46 +-
 .../examples/flexible_job_shop_sat.ipynb      |   58 +-
 .../examples/gate_scheduling_sat.ipynb        |   60 +-
 examples/notebook/examples/golomb8.ipynb      |   24 +-
 examples/notebook/examples/golomb_sat.ipynb   |   32 +-
 examples/notebook/examples/hidato_sat.ipynb   |  124 +-
 .../examples/integer_programming.ipynb        |   58 +-
 .../examples/jobshop_ft06_distance_sat.ipynb  |   60 +-
 .../notebook/examples/jobshop_ft06_sat.ipynb  |   54 +-
 .../jobshop_with_maintenance_sat.ipynb        |   90 +-
 .../notebook/examples/knapsack_2d_sat.ipynb   |  217 +-
 .../examples/line_balancing_sat.ipynb         |  110 +-
 .../examples/linear_assignment_api.ipynb      |   21 +-
 .../examples/linear_programming.ipynb         |   85 +-
 .../examples/magic_sequence_distribute.ipynb  |    9 +-
 .../notebook/examples/maze_escape_sat.ipynb   |   70 +-
 .../no_wait_baking_scheduling_sat.ipynb       |  148 +-
 examples/notebook/examples/nqueens_sat.ipynb  |   31 +-
 .../examples/prize_collecting_tsp_sat.ipynb   |   42 +-
 .../examples/prize_collecting_vrp_sat.ipynb   |   59 +-
 .../notebook/examples/pyflow_example.ipynb    |   47 +-
 examples/notebook/examples/qubo_sat.ipynb     |   12 +-
 examples/notebook/examples/rcpsp_sat.ipynb    |  438 ++-
 .../examples/shift_scheduling_sat.ipynb       |  242 +-
 ...ing_with_setup_release_due_dates_sat.ipynb |  427 +-
 .../notebook/examples/spread_robots_sat.ipynb |   47 +-
 .../examples/steel_mill_slab_sat.ipynb        |  492 +--
 examples/notebook/examples/sudoku_sat.ipynb   |   22 +-
 .../examples/task_allocation_sat.ipynb        |  496 ++-
 .../tasks_and_workers_assignment_sat.ipynb    |   38 +-
 examples/notebook/examples/tsp_sat.ipynb      |   22 +-
 .../examples/vendor_scheduling_sat.ipynb      |   74 +-
 .../examples/wedding_optimal_chart_sat.ipynb  |  113 +-
 .../weighted_latency_problem_sat.ipynb        |   28 +-
 examples/notebook/examples/zebra_sat.ipynb    |   74 +-
 .../assignment_linear_sum_assignment.ipynb    |   30 +-
 .../notebook/graph/assignment_min_flow.ipynb  |   42 +-
 .../notebook/graph/balance_min_flow.ipynb     |   76 +-
 .../graph/simple_max_flow_program.ipynb       |   16 +-
 .../graph/simple_min_cost_flow_program.ipynb  |   15 +-
 .../linear_solver/assignment_groups_mip.ipynb |   33 +-
 .../linear_solver/assignment_mb.ipynb         |   72 +-
 .../linear_solver/assignment_mip.ipynb        |   12 +-
 .../assignment_task_sizes_mip.ipynb           |   25 +-
 .../linear_solver/assignment_teams_mip.ipynb  |   22 +-
 .../linear_solver/basic_example.ipynb         |   28 +-
 .../linear_solver/bin_packing_mb.ipynb        |  102 +-
 .../linear_solver/bin_packing_mip.ipynb       |   52 +-
 .../integer_programming_example.ipynb         |   13 +-
 .../linear_programming_example.ipynb          |   27 +-
 .../linear_solver/mip_var_array.ipynb         |   47 +-
 .../linear_solver/multiple_knapsack_mip.ipynb |   67 +-
 .../linear_solver/simple_lp_program.ipynb     |   29 +-
 .../linear_solver/simple_lp_program_mb.ipynb  |   28 +-
 .../linear_solver/simple_mip_program.ipynb    |   31 +-
 .../linear_solver/simple_mip_program_mb.ipynb |   24 +-
 .../notebook/linear_solver/stigler_diet.ipynb |  304 +-
 .../notebook/pdlp/simple_pdlp_program.ipynb   |   68 +-
 .../notebook/sat/assignment_groups_sat.ipynb  |   17 +-
 examples/notebook/sat/assignment_sat.ipynb    |   73 +-
 .../sat/assignment_task_sizes_sat.ipynb       |   17 +-
 .../notebook/sat/assignment_teams_sat.ipynb   |   12 +-
 .../notebook/sat/assumptions_sample_sat.ipynb |   21 +-
 examples/notebook/sat/bin_packing_sat.ipynb   |  193 +
 .../notebook/sat/binpacking_problem_sat.ipynb |   22 +-
 .../notebook/sat/bool_or_sample_sat.ipynb     |    8 +-
 .../sat/boolean_product_sample_sat.ipynb      |   14 +-
 .../notebook/sat/channeling_sample_sat.ipynb  |   15 +-
 .../notebook/sat/copy_model_sample_sat.ipynb  |   13 +-
 examples/notebook/sat/cp_is_fun_sat.ipynb     |   40 +-
 examples/notebook/sat/cp_sat_example.ipynb    |   26 +-
 .../earliness_tardiness_cost_sample_sat.ipynb |   17 +-
 .../notebook/sat/interval_sample_sat.ipynb    |   22 +-
 .../notebook/sat/literal_sample_sat.ipynb     |    6 +-
 .../notebook/sat/minimal_jobshop_sat.ipynb    |   89 +-
 .../notebook/sat/multiple_knapsack_sat.ipynb  |   64 +-
 .../notebook/sat/no_overlap_sample_sat.ipynb  |   40 +-
 examples/notebook/sat/non_linear_sat.ipynb    |   15 +-
 examples/notebook/sat/nqueens_sat.ipynb       |   24 +-
 examples/notebook/sat/nurses_sat.ipynb        |   30 +-
 .../sat/optional_interval_sample_sat.ipynb    |   25 +-
 .../overlapping_intervals_sample_sat.ipynb    |   35 +-
 .../sat/rabbits_and_pheasants_sat.ipynb       |    8 +-
 .../notebook/sat/ranking_sample_sat.ipynb     |   70 +-
 .../notebook/sat/reified_sample_sat.ipynb     |    7 +-
 .../notebook/sat/schedule_requests_sat.ipynb  |   54 +-
 .../scheduling_with_calendar_sample_sat.ipynb |   20 +-
 .../search_for_all_solutions_sample_sat.ipynb |   13 +-
 .../notebook/sat/simple_sat_program.ipynb     |   14 +-
 .../sat/solution_hinting_sample_sat.ipynb     |   11 +-
 ...nt_intermediate_solutions_sample_sat.ipynb |   17 +-
 .../solve_with_time_limit_sample_sat.ipynb    |   12 +-
 .../sat/step_function_sample_sat.ipynb        |   23 +-
 .../stop_after_n_solutions_sample_sat.ipynb   |   14 +-
 130 files changed, 6534 insertions(+), 6362 deletions(-)
 create mode 100644 examples/notebook/sat/bin_packing_sat.ipynb

diff --git a/examples/notebook/algorithms/knapsack.ipynb b/examples/notebook/algorithms/knapsack.ipynb
index d8c7c861d3..9bdd73f181 100644
--- a/examples/notebook/algorithms/knapsack.ipynb
+++ b/examples/notebook/algorithms/knapsack.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "A simple knapsack problem."
    ]
   },
@@ -82,43 +83,48 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from ortools.algorithms import pywrapknapsack_solver\n",
+    "from ortools.algorithms.python import knapsack_solver\n",
     "\n",
     "\n",
     "def main():\n",
     "    # Create the solver.\n",
-    "    solver = pywrapknapsack_solver.KnapsackSolver(\n",
-    "        pywrapknapsack_solver.KnapsackSolver.\n",
-    "        KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER, 'KnapsackExample')\n",
+    "    solver = knapsack_solver.KnapsackSolver(\n",
+    "        knapsack_solver.SolverType.KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER,\n",
+    "        \"KnapsackExample\",\n",
+    "    )\n",
     "\n",
     "    values = [\n",
-    "        360, 83, 59, 130, 431, 67, 230, 52, 93, 125, 670, 892, 600, 38, 48, 147,\n",
-    "        78, 256, 63, 17, 120, 164, 432, 35, 92, 110, 22, 42, 50, 323, 514, 28,\n",
-    "        87, 73, 78, 15, 26, 78, 210, 36, 85, 189, 274, 43, 33, 10, 19, 389, 276,\n",
-    "        312\n",
+    "        # fmt:off\n",
+    "      360, 83, 59, 130, 431, 67, 230, 52, 93, 125, 670, 892, 600, 38, 48, 147,\n",
+    "      78, 256, 63, 17, 120, 164, 432, 35, 92, 110, 22, 42, 50, 323, 514, 28,\n",
+    "      87, 73, 78, 15, 26, 78, 210, 36, 85, 189, 274, 43, 33, 10, 19, 389, 276,\n",
+    "      312\n",
+    "        # fmt:on\n",
+    "    ]\n",
+    "    weights = [\n",
+    "        # fmt: off\n",
+    "    [7, 0, 30, 22, 80, 94, 11, 81, 70, 64, 59, 18, 0, 36, 3, 8, 15, 42, 9, 0,\n",
+    "     42, 47, 52, 32, 26, 48, 55, 6, 29, 84, 2, 4, 18, 56, 7, 29, 93, 44, 71, 3,\n",
+    "     86, 66, 31, 65, 0, 79, 20, 65, 52, 13],\n",
+    "        # fmt: on\n",
     "    ]\n",
-    "    weights = [[\n",
-    "        7, 0, 30, 22, 80, 94, 11, 81, 70, 64, 59, 18, 0, 36, 3, 8, 15, 42, 9, 0,\n",
-    "        42, 47, 52, 32, 26, 48, 55, 6, 29, 84, 2, 4, 18, 56, 7, 29, 93, 44, 71,\n",
-    "        3, 86, 66, 31, 65, 0, 79, 20, 65, 52, 13\n",
-    "    ]]\n",
     "    capacities = [850]\n",
     "\n",
-    "    solver.Init(values, weights, capacities)\n",
-    "    computed_value = solver.Solve()\n",
+    "    solver.init(values, weights, capacities)\n",
+    "    computed_value = solver.solve()\n",
     "\n",
     "    packed_items = []\n",
     "    packed_weights = []\n",
     "    total_weight = 0\n",
-    "    print('Total value =', computed_value)\n",
+    "    print(\"Total value =\", computed_value)\n",
     "    for i in range(len(values)):\n",
-    "        if solver.BestSolutionContains(i):\n",
+    "        if solver.best_solution_contains(i):\n",
     "            packed_items.append(i)\n",
     "            packed_weights.append(weights[0][i])\n",
     "            total_weight += weights[0][i]\n",
-    "    print('Total weight:', total_weight)\n",
-    "    print('Packed items:', packed_items)\n",
-    "    print('Packed_weights:', packed_weights)\n",
+    "    print(\"Total weight:\", total_weight)\n",
+    "    print(\"Packed items:\", packed_items)\n",
+    "    print(\"Packed_weights:\", packed_weights)\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/algorithms/simple_knapsack_program.ipynb b/examples/notebook/algorithms/simple_knapsack_program.ipynb
index 871ce6577c..9b7d94f209 100644
--- a/examples/notebook/algorithms/simple_knapsack_program.ipynb
+++ b/examples/notebook/algorithms/simple_knapsack_program.ipynb
@@ -83,27 +83,29 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from ortools.algorithms import pywrapknapsack_solver\n",
+    "from ortools.algorithms.python import knapsack_solver\n",
     "\n",
     "\n",
     "def main():\n",
     "    # Create the solver.\n",
-    "    solver = pywrapknapsack_solver.KnapsackSolver(\n",
-    "        pywrapknapsack_solver.KnapsackSolver.\n",
-    "        KNAPSACK_DYNAMIC_PROGRAMMING_SOLVER, \"test\")\n",
+    "    solver = knapsack_solver.KnapsackSolver(\n",
+    "        knapsack_solver.SolverType.KNAPSACK_DYNAMIC_PROGRAMMING_SOLVER,\n",
+    "        \"test\",\n",
+    "    )\n",
     "\n",
-    "    weights = [[\n",
-    "        565, 406, 194, 130, 435, 367, 230, 315, 393, 125, 670, 892, 600, 293,\n",
-    "        712, 147, 421, 255\n",
-    "    ]]\n",
+    "    weights = [\n",
+    "        # fmt:off\n",
+    "    [565, 406, 194, 130, 435, 367, 230, 315, 393, 125, 670, 892, 600, 293, 712, 147, 421, 255],\n",
+    "        # fmt:on\n",
+    "    ]\n",
     "    capacities = [850]\n",
     "    values = weights[0]\n",
     "\n",
-    "    solver.Init(values, weights, capacities)\n",
-    "    computed_value = solver.Solve()\n",
+    "    solver.init(values, weights, capacities)\n",
+    "    computed_value = solver.solve()\n",
     "\n",
     "    packed_items = [\n",
-    "        x for x in range(0, len(weights[0])) if solver.BestSolutionContains(x)\n",
+    "        x for x in range(0, len(weights[0])) if solver.best_solution_contains(x)\n",
     "    ]\n",
     "    packed_weights = [weights[0][i] for i in packed_items]\n",
     "\n",
diff --git a/examples/notebook/constraint_solver/cp_is_fun_cp.ipynb b/examples/notebook/constraint_solver/cp_is_fun_cp.ipynb
index 76f3a21b6c..19e95c9817 100644
--- a/examples/notebook/constraint_solver/cp_is_fun_cp.ipynb
+++ b/examples/notebook/constraint_solver/cp_is_fun_cp.ipynb
@@ -93,23 +93,23 @@
     "\n",
     "def main():\n",
     "    # Constraint programming engine\n",
-    "    solver = pywrapcp.Solver('CP is fun!')\n",
+    "    solver = pywrapcp.Solver(\"CP is fun!\")\n",
     "\n",
     "    base = 10\n",
     "\n",
     "    # Decision variables.\n",
     "    digits = list(range(0, base))\n",
     "    digits_without_zero = list(range(1, base))\n",
-    "    c = solver.IntVar(digits_without_zero, 'C')\n",
-    "    p = solver.IntVar(digits, 'P')\n",
-    "    i = solver.IntVar(digits_without_zero, 'I')\n",
-    "    s = solver.IntVar(digits, 'S')\n",
-    "    f = solver.IntVar(digits_without_zero, 'F')\n",
-    "    u = solver.IntVar(digits, 'U')\n",
-    "    n = solver.IntVar(digits, 'N')\n",
-    "    t = solver.IntVar(digits_without_zero, 'T')\n",
-    "    r = solver.IntVar(digits, 'R')\n",
-    "    e = solver.IntVar(digits, 'E')\n",
+    "    c = solver.IntVar(digits_without_zero, \"C\")\n",
+    "    p = solver.IntVar(digits, \"P\")\n",
+    "    i = solver.IntVar(digits_without_zero, \"I\")\n",
+    "    s = solver.IntVar(digits, \"S\")\n",
+    "    f = solver.IntVar(digits_without_zero, \"F\")\n",
+    "    u = solver.IntVar(digits, \"U\")\n",
+    "    n = solver.IntVar(digits, \"N\")\n",
+    "    t = solver.IntVar(digits_without_zero, \"T\")\n",
+    "    r = solver.IntVar(digits, \"R\")\n",
+    "    e = solver.IntVar(digits, \"E\")\n",
     "\n",
     "    # We need to group variables in a list to use the constraint AllDifferent.\n",
     "    letters = [c, p, i, s, f, u, n, t, r, e]\n",
@@ -121,8 +121,10 @@
     "    solver.Add(solver.AllDifferent(letters))\n",
     "\n",
     "    # CP + IS + FUN = TRUE\n",
-    "    solver.Add(p + s + n + base * (c + i + u) + base * base * f == e +\n",
-    "               base * u + base * base * r + base * base * base * t)\n",
+    "    solver.Add(\n",
+    "        p + s + n + base * (c + i + u) + base * base * f\n",
+    "        == e + base * u + base * base * r + base * base * base * t\n",
+    "    )\n",
     "\n",
     "    solution_count = 0\n",
     "    db = solver.Phase(letters, solver.INT_VAR_DEFAULT, solver.INT_VALUE_DEFAULT)\n",
@@ -130,13 +132,22 @@
     "    while solver.NextSolution():\n",
     "        print(letters)\n",
     "        # Is CP + IS + FUN = TRUE?\n",
-    "        assert (base * c.Value() + p.Value() + base * i.Value() + s.Value() +\n",
-    "                base * base * f.Value() + base * u.Value() +\n",
-    "                n.Value() == base * base * base * t.Value() +\n",
-    "                base * base * r.Value() + base * u.Value() + e.Value())\n",
+    "        assert (\n",
+    "            base * c.Value()\n",
+    "            + p.Value()\n",
+    "            + base * i.Value()\n",
+    "            + s.Value()\n",
+    "            + base * base * f.Value()\n",
+    "            + base * u.Value()\n",
+    "            + n.Value()\n",
+    "            == base * base * base * t.Value()\n",
+    "            + base * base * r.Value()\n",
+    "            + base * u.Value()\n",
+    "            + e.Value()\n",
+    "        )\n",
     "        solution_count += 1\n",
     "    solver.EndSearch()\n",
-    "    print(f'Number of solutions found: {solution_count}')\n",
+    "    print(f\"Number of solutions found: {solution_count}\")\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/constraint_solver/cvrptw_break.ipynb b/examples/notebook/constraint_solver/cvrptw_break.ipynb
index 6914f32231..2bc9441182 100644
--- a/examples/notebook/constraint_solver/cvrptw_break.ipynb
+++ b/examples/notebook/constraint_solver/cvrptw_break.ipynb
@@ -100,81 +100,91 @@
     "    \"\"\"Stores the data for the problem.\"\"\"\n",
     "    data = {}\n",
     "    # Locations in block unit\n",
-    "    locations_ = \\\n",
-    "            [(4, 4),  # depot\n",
-    "             (2, 0), (8, 0),  # locations to visit\n",
-    "             (0, 1), (1, 1),\n",
-    "             (5, 2), (7, 2),\n",
-    "             (3, 3), (6, 3),\n",
-    "             (5, 5), (8, 5),\n",
-    "             (1, 6), (2, 6),\n",
-    "             (3, 7), (6, 7),\n",
-    "             (0, 8), (7, 8)]\n",
+    "    locations_ = [\n",
+    "        # fmt: off\n",
+    "      (4, 4),  # depot\n",
+    "      (2, 0), (8, 0),  # locations to visit\n",
+    "      (0, 1), (1, 1),\n",
+    "      (5, 2), (7, 2),\n",
+    "      (3, 3), (6, 3),\n",
+    "      (5, 5), (8, 5),\n",
+    "      (1, 6), (2, 6),\n",
+    "      (3, 7), (6, 7),\n",
+    "      (0, 8), (7, 8),\n",
+    "        # fmt: on\n",
+    "    ]\n",
     "    # Compute locations in meters using the block dimension defined as follow\n",
     "    # Manhattan average block: 750ft x 264ft -> 228m x 80m\n",
     "    # here we use: 114m x 80m city block\n",
     "    # src: https://nyti.ms/2GDoRIe \"NY Times: Know Your distance\"\n",
-    "    data['locations'] = [(l[0] * 114, l[1] * 80) for l in locations_]\n",
-    "    data['numlocations_'] = len(data['locations'])\n",
-    "    data['time_windows'] = \\\n",
-    "          [(0, 0),\n",
-    "           (75, 85), (75, 85),  # 1, 2\n",
-    "           (60, 70), (45, 55),  # 3, 4\n",
-    "           (0, 8), (50, 60),  # 5, 6\n",
-    "           (0, 10), (10, 20),  # 7, 8\n",
-    "           (0, 10), (75, 85),  # 9, 10\n",
-    "           (85, 95), (5, 15),  # 11, 12\n",
-    "           (15, 25), (10, 20),  # 13, 14\n",
-    "           (45, 55), (30, 40)]  # 15, 16\n",
-    "    data['demands'] = \\\n",
-    "          [0,  # depot\n",
-    "           1, 1,  # 1, 2\n",
-    "           2, 4,  # 3, 4\n",
-    "           2, 4,  # 5, 6\n",
-    "           8, 8,  # 7, 8\n",
-    "           1, 2,  # 9,10\n",
-    "           1, 2,  # 11,12\n",
-    "           4, 4,  # 13, 14\n",
-    "           8, 8]  # 15, 16\n",
-    "    data['time_per_demand_unit'] = 5  # 5 minutes/unit\n",
-    "    data['num_vehicles'] = 4\n",
-    "    data['breaks'] = [(2, False), (2, False), (2, False), (2, False)]\n",
-    "    data['vehicle_capacity'] = 15\n",
-    "    data['vehicle_speed'] = 83  # Travel speed: 5km/h converted in m/min\n",
-    "    data['depot'] = 0\n",
+    "    data[\"locations\"] = [(l[0] * 114, l[1] * 80) for l in locations_]\n",
+    "    data[\"numlocations_\"] = len(data[\"locations\"])\n",
+    "    data[\"time_windows\"] = [\n",
+    "        # fmt: off\n",
+    "      (0, 0),  # depot\n",
+    "      (75, 85), (75, 85),  #  1,  2\n",
+    "      (60, 70), (45, 55),  #  3,  4\n",
+    "      (0, 8), (50, 60),    #  5,  6\n",
+    "      (0, 10), (10, 20),   #  7,  8\n",
+    "      (0, 10), (75, 85),   #  9, 10\n",
+    "      (85, 95), (5, 15),   # 11, 12\n",
+    "      (15, 25), (10, 20),  # 13, 14\n",
+    "      (45, 55), (30, 40),\n",
+    "        # 15, 16\n",
+    "        # fmt: on\n",
+    "    ]\n",
+    "    data[\"demands\"] = [\n",
+    "        # fmt: off\n",
+    "      0,     # depot\n",
+    "      1, 1,  #  1,  2\n",
+    "      2, 4,  #  3,  4\n",
+    "      2, 4,  #  5,  6\n",
+    "      8, 8,  #  7,  8\n",
+    "      1, 2,  #  9, 10\n",
+    "      1, 2,  # 11, 12\n",
+    "      4, 4,  # 13, 14\n",
+    "      8, 8,\n",
+    "        # 15, 16\n",
+    "        # fmt: on\n",
+    "    ]\n",
+    "    data[\"time_per_demand_unit\"] = 5  # 5 minutes/unit\n",
+    "    data[\"num_vehicles\"] = 4\n",
+    "    data[\"breaks\"] = [(2, False), (2, False), (2, False), (2, False)]\n",
+    "    data[\"vehicle_capacity\"] = 15\n",
+    "    data[\"vehicle_speed\"] = 83  # Travel speed: 5km/h converted in m/min\n",
+    "    data[\"depot\"] = 0\n",
     "    return data\n",
     "\n",
     "\n",
     "def manhattan_distance(position_1, position_2):\n",
     "    \"\"\"Computes the Manhattan distance between two points.\"\"\"\n",
-    "    return (abs(position_1[0] - position_2[0]) +\n",
-    "            abs(position_1[1] - position_2[1]))\n",
+    "    return abs(position_1[0] - position_2[0]) + abs(position_1[1] - position_2[1])\n",
     "\n",
     "\n",
     "def create_distance_evaluator(data):\n",
     "    \"\"\"Creates callback to return distance between points.\"\"\"\n",
     "    distances_ = {}\n",
     "    # precompute distance between location to have distance callback in O(1)\n",
-    "    for from_node in range(data['numlocations_']):\n",
+    "    for from_node in range(data[\"numlocations_\"]):\n",
     "        distances_[from_node] = {}\n",
-    "        for to_node in range(data['numlocations_']):\n",
+    "        for to_node in range(data[\"numlocations_\"]):\n",
     "            if from_node == to_node:\n",
     "                distances_[from_node][to_node] = 0\n",
     "            else:\n",
-    "                distances_[from_node][to_node] = (manhattan_distance(\n",
-    "                    data['locations'][from_node], data['locations'][to_node]))\n",
+    "                distances_[from_node][to_node] = manhattan_distance(\n",
+    "                    data[\"locations\"][from_node], data[\"locations\"][to_node]\n",
+    "                )\n",
     "\n",
     "    def distance_evaluator(manager, from_node, to_node):\n",
     "        \"\"\"Returns the manhattan distance between the two nodes.\"\"\"\n",
-    "        return distances_[manager.IndexToNode(from_node)][manager.IndexToNode(\n",
-    "            to_node)]\n",
+    "        return distances_[manager.IndexToNode(from_node)][manager.IndexToNode(to_node)]\n",
     "\n",
     "    return distance_evaluator\n",
     "\n",
     "\n",
     "def create_demand_evaluator(data):\n",
     "    \"\"\"Creates callback to get demands at each location.\"\"\"\n",
-    "    demands_ = data['demands']\n",
+    "    demands_ = data[\"demands\"]\n",
     "\n",
     "    def demand_evaluator(manager, node):\n",
     "        \"\"\"Returns the demand of the current node.\"\"\"\n",
@@ -185,13 +195,14 @@
     "\n",
     "def add_capacity_constraints(routing, data, demand_evaluator_index):\n",
     "    \"\"\"Adds capacity constraint.\"\"\"\n",
-    "    capacity = 'Capacity'\n",
+    "    capacity = \"Capacity\"\n",
     "    routing.AddDimension(\n",
     "        demand_evaluator_index,\n",
     "        0,  # null capacity slack\n",
-    "        data['vehicle_capacity'],\n",
+    "        data[\"vehicle_capacity\"],\n",
     "        True,  # start cumul to zero\n",
-    "        capacity)\n",
+    "        capacity,\n",
+    "    )\n",
     "\n",
     "\n",
     "def create_time_evaluator(data):\n",
@@ -199,63 +210,68 @@
     "\n",
     "    def service_time(data, node):\n",
     "        \"\"\"Gets the service time for the specified location.\"\"\"\n",
-    "        return data['demands'][node] * data['time_per_demand_unit']\n",
+    "        return data[\"demands\"][node] * data[\"time_per_demand_unit\"]\n",
     "\n",
     "    def travel_time(data, from_node, to_node):\n",
     "        \"\"\"Gets the travel times between two locations.\"\"\"\n",
     "        if from_node == to_node:\n",
     "            travel_time = 0\n",
     "        else:\n",
-    "            travel_time = manhattan_distance(\n",
-    "                data['locations'][from_node],\n",
-    "                data['locations'][to_node]) / data['vehicle_speed']\n",
+    "            travel_time = (\n",
+    "                manhattan_distance(\n",
+    "                    data[\"locations\"][from_node], data[\"locations\"][to_node]\n",
+    "                )\n",
+    "                / data[\"vehicle_speed\"]\n",
+    "            )\n",
     "        return travel_time\n",
     "\n",
     "    total_time_ = {}\n",
     "    # precompute total time to have time callback in O(1)\n",
-    "    for from_node in range(data['numlocations_']):\n",
+    "    for from_node in range(data[\"numlocations_\"]):\n",
     "        total_time_[from_node] = {}\n",
-    "        for to_node in range(data['numlocations_']):\n",
+    "        for to_node in range(data[\"numlocations_\"]):\n",
     "            if from_node == to_node:\n",
     "                total_time_[from_node][to_node] = 0\n",
     "            else:\n",
     "                total_time_[from_node][to_node] = int(\n",
-    "                    service_time(data, from_node) +\n",
-    "                    travel_time(data, from_node, to_node))\n",
+    "                    service_time(data, from_node)\n",
+    "                    + travel_time(data, from_node, to_node)\n",
+    "                )\n",
     "\n",
     "    def time_evaluator(manager, from_node, to_node):\n",
     "        \"\"\"Returns the total time between the two nodes.\"\"\"\n",
-    "        return total_time_[manager.IndexToNode(from_node)][manager.IndexToNode(\n",
-    "            to_node)]\n",
+    "        return total_time_[manager.IndexToNode(from_node)][manager.IndexToNode(to_node)]\n",
     "\n",
     "    return time_evaluator\n",
     "\n",
     "\n",
     "def add_time_window_constraints(routing, manager, data, time_evaluator_index):\n",
     "    \"\"\"Add Global Span constraint.\"\"\"\n",
-    "    time = 'Time'\n",
+    "    time = \"Time\"\n",
     "    horizon = 120\n",
     "    routing.AddDimension(\n",
     "        time_evaluator_index,\n",
     "        horizon,  # allow waiting time\n",
     "        horizon,  # maximum time per vehicle\n",
     "        False,  # don't force start cumul to zero\n",
-    "        time)\n",
+    "        time,\n",
+    "    )\n",
     "    time_dimension = routing.GetDimensionOrDie(time)\n",
     "    # Add time window constraints for each location except depot\n",
     "    # and 'copy' the slack var in the solution object (aka Assignment) to print it\n",
-    "    for location_idx, time_window in enumerate(data['time_windows']):\n",
-    "        if location_idx == data['depot']:\n",
+    "    for location_idx, time_window in enumerate(data[\"time_windows\"]):\n",
+    "        if location_idx == data[\"depot\"]:\n",
     "            continue\n",
     "        index = manager.NodeToIndex(location_idx)\n",
     "        time_dimension.CumulVar(index).SetRange(time_window[0], time_window[1])\n",
     "        routing.AddToAssignment(time_dimension.SlackVar(index))\n",
     "    # Add time window constraints for each vehicle start node\n",
     "    # and 'copy' the slack var in the solution object (aka Assignment) to print it\n",
-    "    for vehicle_id in range(data['num_vehicles']):\n",
+    "    for vehicle_id in range(data[\"num_vehicles\"]):\n",
     "        index = routing.Start(vehicle_id)\n",
-    "        time_dimension.CumulVar(index).SetRange(data['time_windows'][0][0],\n",
-    "                                                data['time_windows'][0][1])\n",
+    "        time_dimension.CumulVar(index).SetRange(\n",
+    "            data[\"time_windows\"][0][0], data[\"time_windows\"][0][1]\n",
+    "        )\n",
     "        routing.AddToAssignment(time_dimension.SlackVar(index))\n",
     "        # The time window at the end node was impliclty set in the time dimension\n",
     "        # definition to be [0, horizon].\n",
@@ -263,28 +279,32 @@
     "        # be added to the assignment.\n",
     "\n",
     "\n",
-    "def print_solution(data, manager, routing, assignment):  # pylint:disable=too-many-locals\n",
+    "def print_solution(\n",
+    "    data, manager, routing, assignment\n",
+    "):  # pylint:disable=too-many-locals\n",
     "    \"\"\"Prints assignment on console.\"\"\"\n",
-    "    print(f'Objective: {assignment.ObjectiveValue()}')\n",
+    "    print(f\"Objective: {assignment.ObjectiveValue()}\")\n",
     "\n",
-    "    print('Breaks:')\n",
+    "    print(\"Breaks:\")\n",
     "    intervals = assignment.IntervalVarContainer()\n",
     "    for i in range(intervals.Size()):\n",
     "        brk = intervals.Element(i)\n",
     "        if brk.PerformedValue() == 1:\n",
-    "            print(f'{brk.Var().Name()}:'\n",
-    "                  f' Start({brk.StartValue()}) Duration({brk.DurationValue()})')\n",
+    "            print(\n",
+    "                f\"{brk.Var().Name()}:\"\n",
+    "                f\" Start({brk.StartValue()}) Duration({brk.DurationValue()})\"\n",
+    "            )\n",
     "        else:\n",
-    "            print(f'{brk.Var().Name()}: Unperformed')\n",
+    "            print(f\"{brk.Var().Name()}: Unperformed\")\n",
     "\n",
     "    total_distance = 0\n",
     "    total_load = 0\n",
     "    total_time = 0\n",
-    "    capacity_dimension = routing.GetDimensionOrDie('Capacity')\n",
-    "    time_dimension = routing.GetDimensionOrDie('Time')\n",
-    "    for vehicle_id in range(data['num_vehicles']):\n",
+    "    capacity_dimension = routing.GetDimensionOrDie(\"Capacity\")\n",
+    "    time_dimension = routing.GetDimensionOrDie(\"Time\")\n",
+    "    for vehicle_id in range(data[\"num_vehicles\"]):\n",
     "        index = routing.Start(vehicle_id)\n",
-    "        plan_output = f'Route for vehicle {vehicle_id}:\\n'\n",
+    "        plan_output = f\"Route for vehicle {vehicle_id}:\\n\"\n",
     "        distance = 0\n",
     "        while not routing.IsEnd(index):\n",
     "            load_var = capacity_dimension.CumulVar(index)\n",
@@ -292,32 +312,33 @@
     "            slack_var = time_dimension.SlackVar(index)\n",
     "            node = manager.IndexToNode(index)\n",
     "            plan_output += (\n",
-    "                f' {node}'\n",
-    "                f' Load({assignment.Value(load_var)})'\n",
-    "                f' Time({assignment.Min(time_var)}, {assignment.Max(time_var)})'\n",
-    "                f' Slack({assignment.Min(slack_var)}, {assignment.Max(slack_var)})'\n",
-    "                ' ->')\n",
+    "                f\" {node}\"\n",
+    "                f\" Load({assignment.Value(load_var)})\"\n",
+    "                f\" Time({assignment.Min(time_var)}, {assignment.Max(time_var)})\"\n",
+    "                f\" Slack({assignment.Min(slack_var)}, {assignment.Max(slack_var)})\"\n",
+    "                \" ->\"\n",
+    "            )\n",
     "            previous_index = index\n",
     "            index = assignment.Value(routing.NextVar(index))\n",
-    "            distance += routing.GetArcCostForVehicle(previous_index, index,\n",
-    "                                                     vehicle_id)\n",
+    "            distance += routing.GetArcCostForVehicle(previous_index, index, vehicle_id)\n",
     "        load_var = capacity_dimension.CumulVar(index)\n",
     "        time_var = time_dimension.CumulVar(index)\n",
     "        node = manager.IndexToNode(index)\n",
     "        plan_output += (\n",
-    "            f' {node}'\n",
-    "            f' Load({assignment.Value(load_var)})'\n",
-    "            f' Time({assignment.Min(time_var)}, {assignment.Max(time_var)})\\n')\n",
-    "        plan_output += f'Distance of the route: {distance}m\\n'\n",
-    "        plan_output += f'Load of the route: {assignment.Value(load_var)}\\n'\n",
-    "        plan_output += f'Time of the route: {assignment.Value(time_var)}\\n'\n",
+    "            f\" {node}\"\n",
+    "            f\" Load({assignment.Value(load_var)})\"\n",
+    "            f\" Time({assignment.Min(time_var)}, {assignment.Max(time_var)})\\n\"\n",
+    "        )\n",
+    "        plan_output += f\"Distance of the route: {distance}m\\n\"\n",
+    "        plan_output += f\"Load of the route: {assignment.Value(load_var)}\\n\"\n",
+    "        plan_output += f\"Time of the route: {assignment.Value(time_var)}\\n\"\n",
     "        print(plan_output)\n",
     "        total_distance += distance\n",
     "        total_load += assignment.Value(load_var)\n",
     "        total_time += assignment.Value(time_var)\n",
-    "    print(f'Total Distance of all routes: {total_distance}m')\n",
-    "    print(f'Total Load of all routes: {total_load}')\n",
-    "    print(f'Total Time of all routes: {total_time}min')\n",
+    "    print(f\"Total Distance of all routes: {total_distance}m\")\n",
+    "    print(f\"Total Load of all routes: {total_load}\")\n",
+    "    print(f\"Total Time of all routes: {total_time}min\")\n",
     "\n",
     "\n",
     "def main():\n",
@@ -326,50 +347,57 @@
     "    data = create_data_model()\n",
     "\n",
     "    # Create the routing index manager\n",
-    "    manager = pywrapcp.RoutingIndexManager(data['numlocations_'],\n",
-    "                                           data['num_vehicles'], data['depot'])\n",
+    "    manager = pywrapcp.RoutingIndexManager(\n",
+    "        data[\"numlocations_\"], data[\"num_vehicles\"], data[\"depot\"]\n",
+    "    )\n",
     "\n",
     "    # Create Routing Model\n",
     "    routing = pywrapcp.RoutingModel(manager)\n",
     "\n",
     "    # Define weight of each edge\n",
     "    distance_evaluator_index = routing.RegisterTransitCallback(\n",
-    "        functools.partial(create_distance_evaluator(data), manager))\n",
+    "        functools.partial(create_distance_evaluator(data), manager)\n",
+    "    )\n",
     "    routing.SetArcCostEvaluatorOfAllVehicles(distance_evaluator_index)\n",
     "\n",
     "    # Add Capacity constraint\n",
     "    demand_evaluator_index = routing.RegisterUnaryTransitCallback(\n",
-    "        functools.partial(create_demand_evaluator(data), manager))\n",
+    "        functools.partial(create_demand_evaluator(data), manager)\n",
+    "    )\n",
     "    add_capacity_constraints(routing, data, demand_evaluator_index)\n",
     "\n",
     "    # Add Time Window constraint\n",
     "    time_evaluator_index = routing.RegisterTransitCallback(\n",
-    "        functools.partial(create_time_evaluator(data), manager))\n",
+    "        functools.partial(create_time_evaluator(data), manager)\n",
+    "    )\n",
     "    add_time_window_constraints(routing, manager, data, time_evaluator_index)\n",
     "\n",
     "    # Add breaks\n",
-    "    time_dimension = routing.GetDimensionOrDie('Time')\n",
+    "    time_dimension = routing.GetDimensionOrDie(\"Time\")\n",
     "    node_visit_transit = {}\n",
     "    for index in range(routing.Size()):\n",
     "        node = manager.IndexToNode(index)\n",
-    "        node_visit_transit[index] = int(data['demands'][node] *\n",
-    "                                        data['time_per_demand_unit'])\n",
+    "        node_visit_transit[index] = int(\n",
+    "            data[\"demands\"][node] * data[\"time_per_demand_unit\"]\n",
+    "        )\n",
     "\n",
     "    break_intervals = {}\n",
-    "    for v in range(data['num_vehicles']):\n",
-    "        vehicle_break = data['breaks'][v]\n",
+    "    for v in range(data[\"num_vehicles\"]):\n",
+    "        vehicle_break = data[\"breaks\"][v]\n",
     "        break_intervals[v] = [\n",
-    "            routing.solver().FixedDurationIntervalVar(15, 100, vehicle_break[0],\n",
-    "                                                      vehicle_break[1],\n",
-    "                                                      f'Break for vehicle {v}')\n",
+    "            routing.solver().FixedDurationIntervalVar(\n",
+    "                15, 100, vehicle_break[0], vehicle_break[1], f\"Break for vehicle {v}\"\n",
+    "            )\n",
     "        ]\n",
-    "        time_dimension.SetBreakIntervalsOfVehicle(break_intervals[v], v,\n",
-    "                                                  node_visit_transit.values())\n",
+    "        time_dimension.SetBreakIntervalsOfVehicle(\n",
+    "            break_intervals[v], v, node_visit_transit.values()\n",
+    "        )\n",
     "\n",
     "    # Setting first solution heuristic (cheapest addition).\n",
     "    search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n",
     "    search_parameters.first_solution_strategy = (\n",
-    "        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)  # pylint: disable=no-member\n",
+    "        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC\n",
+    "    )  # pylint: disable=no-member\n",
     "\n",
     "    # Solve the problem.\n",
     "    assignment = routing.SolveWithParameters(search_parameters)\n",
@@ -378,7 +406,7 @@
     "    if assignment:\n",
     "        print_solution(data, manager, routing, assignment)\n",
     "    else:\n",
-    "        print('No solution found!')\n",
+    "        print(\"No solution found!\")\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/constraint_solver/nqueens_cp.ipynb b/examples/notebook/constraint_solver/nqueens_cp.ipynb
index fc48174720..890cda4ff3 100644
--- a/examples/notebook/constraint_solver/nqueens_cp.ipynb
+++ b/examples/notebook/constraint_solver/nqueens_cp.ipynb
@@ -89,13 +89,11 @@
     "\n",
     "def main(board_size):\n",
     "    # Creates the solver.\n",
-    "    solver = pywrapcp.Solver('n-queens')\n",
+    "    solver = pywrapcp.Solver(\"n-queens\")\n",
     "\n",
     "    # Creates the variables.\n",
     "    # The array index is the column, and the value is the row.\n",
-    "    queens = [\n",
-    "        solver.IntVar(0, board_size - 1, f'x{i}') for i in range(board_size)\n",
-    "    ]\n",
+    "    queens = [solver.IntVar(0, board_size - 1, f\"x{i}\") for i in range(board_size)]\n",
     "\n",
     "    # Creates the constraints.\n",
     "    # All rows must be different.\n",
@@ -105,8 +103,7 @@
     "    solver.Add(solver.AllDifferent([queens[i] + i for i in range(board_size)]))\n",
     "    solver.Add(solver.AllDifferent([queens[i] - i for i in range(board_size)]))\n",
     "\n",
-    "    db = solver.Phase(queens, solver.CHOOSE_FIRST_UNBOUND,\n",
-    "                      solver.ASSIGN_MIN_VALUE)\n",
+    "    db = solver.Phase(queens, solver.CHOOSE_FIRST_UNBOUND, solver.ASSIGN_MIN_VALUE)\n",
     "\n",
     "    # Iterates through the solutions, displaying each.\n",
     "    num_solutions = 0\n",
@@ -117,20 +114,20 @@
     "            for j in range(board_size):\n",
     "                if queens[j].Value() == i:\n",
     "                    # There is a queen in column j, row i.\n",
-    "                    print('Q', end=' ')\n",
+    "                    print(\"Q\", end=\" \")\n",
     "                else:\n",
-    "                    print('_', end=' ')\n",
+    "                    print(\"_\", end=\" \")\n",
     "            print()\n",
     "        print()\n",
     "        num_solutions += 1\n",
     "    solver.EndSearch()\n",
     "\n",
     "    # Statistics.\n",
-    "    print('\\nStatistics')\n",
-    "    print(f'  failures: {solver.Failures()}')\n",
-    "    print(f'  branches: {solver.Branches()}')\n",
-    "    print(f'  wall time: {solver.WallTime()} ms')\n",
-    "    print(f'  Solutions found: {num_solutions}')\n",
+    "    print(\"\\nStatistics\")\n",
+    "    print(f\"  failures: {solver.Failures()}\")\n",
+    "    print(f\"  branches: {solver.Branches()}\")\n",
+    "    print(f\"  wall time: {solver.WallTime()} ms\")\n",
+    "    print(f\"  Solutions found: {num_solutions}\")\n",
     "\n",
     "\n",
     "# By default, solve the 8x8 problem.\n",
diff --git a/examples/notebook/constraint_solver/simple_cp_program.ipynb b/examples/notebook/constraint_solver/simple_cp_program.ipynb
index 2cf555b238..cfb85a3c86 100644
--- a/examples/notebook/constraint_solver/simple_cp_program.ipynb
+++ b/examples/notebook/constraint_solver/simple_cp_program.ipynb
@@ -89,37 +89,38 @@
     "def main():\n",
     "    \"\"\"Entry point of the program.\"\"\"\n",
     "    # Instantiate the solver.\n",
-    "    solver = pywrapcp.Solver('CPSimple')\n",
+    "    solver = pywrapcp.Solver(\"CPSimple\")\n",
     "\n",
     "    # Create the variables.\n",
     "    num_vals = 3\n",
-    "    x = solver.IntVar(0, num_vals - 1, 'x')\n",
-    "    y = solver.IntVar(0, num_vals - 1, 'y')\n",
-    "    z = solver.IntVar(0, num_vals - 1, 'z')\n",
+    "    x = solver.IntVar(0, num_vals - 1, \"x\")\n",
+    "    y = solver.IntVar(0, num_vals - 1, \"y\")\n",
+    "    z = solver.IntVar(0, num_vals - 1, \"z\")\n",
     "\n",
     "    # Constraint 0: x != y.\n",
     "    solver.Add(x != y)\n",
-    "    print('Number of constraints: ', solver.Constraints())\n",
+    "    print(\"Number of constraints: \", solver.Constraints())\n",
     "\n",
     "    # Solve the problem.\n",
-    "    decision_builder = solver.Phase([x, y, z], solver.CHOOSE_FIRST_UNBOUND,\n",
-    "                                    solver.ASSIGN_MIN_VALUE)\n",
+    "    decision_builder = solver.Phase(\n",
+    "        [x, y, z], solver.CHOOSE_FIRST_UNBOUND, solver.ASSIGN_MIN_VALUE\n",
+    "    )\n",
     "\n",
     "    # Print solution on console.\n",
     "    count = 0\n",
     "    solver.NewSearch(decision_builder)\n",
     "    while solver.NextSolution():\n",
     "        count += 1\n",
-    "        solution = f'Solution {count}:\\n'\n",
+    "        solution = f\"Solution {count}:\\n\"\n",
     "        for var in [x, y, z]:\n",
-    "            solution += f' {var.Name()} = {var.Value()}'\n",
+    "            solution += f\" {var.Name()} = {var.Value()}\"\n",
     "        print(solution)\n",
     "    solver.EndSearch()\n",
-    "    print(f'Number of solutions found: {count}')\n",
+    "    print(f\"Number of solutions found: {count}\")\n",
     "\n",
-    "    print('Advanced usage:')\n",
-    "    print(f'Problem solved in {solver.WallTime()}ms')\n",
-    "    print(f'Memory usage: {pywrapcp.Solver.MemoryUsage()}bytes')\n",
+    "    print(\"Advanced usage:\")\n",
+    "    print(f\"Problem solved in {solver.WallTime()}ms\")\n",
+    "    print(f\"Memory usage: {pywrapcp.Solver.MemoryUsage()}bytes\")\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/constraint_solver/simple_routing_program.ipynb b/examples/notebook/constraint_solver/simple_routing_program.ipynb
index df39ff5edb..f513f5b927 100644
--- a/examples/notebook/constraint_solver/simple_routing_program.ipynb
+++ b/examples/notebook/constraint_solver/simple_routing_program.ipynb
@@ -100,7 +100,6 @@
     "    # Create Routing Model.\n",
     "    routing = pywrapcp.RoutingModel(manager)\n",
     "\n",
-    "\n",
     "    # Create and register a transit callback.\n",
     "    def distance_callback(from_index, to_index):\n",
     "        \"\"\"Returns the absolute difference between the two nodes.\"\"\"\n",
@@ -117,23 +116,24 @@
     "    # Setting first solution heuristic.\n",
     "    search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n",
     "    search_parameters.first_solution_strategy = (\n",
-    "        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)  # pylint: disable=no-member\n",
+    "        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC\n",
+    "    )  # pylint: disable=no-member\n",
     "\n",
     "    # Solve the problem.\n",
     "    assignment = routing.SolveWithParameters(search_parameters)\n",
     "\n",
     "    # Print solution on console.\n",
-    "    print(f'Objective: {assignment.ObjectiveValue()}')\n",
+    "    print(f\"Objective: {assignment.ObjectiveValue()}\")\n",
     "    index = routing.Start(0)\n",
-    "    plan_output = 'Route for vehicle 0:\\n'\n",
+    "    plan_output = \"Route for vehicle 0:\\n\"\n",
     "    route_distance = 0\n",
     "    while not routing.IsEnd(index):\n",
-    "        plan_output += f'{manager.IndexToNode(index)} -> '\n",
+    "        plan_output += f\"{manager.IndexToNode(index)} -> \"\n",
     "        previous_index = index\n",
     "        index = assignment.Value(routing.NextVar(index))\n",
     "        route_distance += routing.GetArcCostForVehicle(previous_index, index, 0)\n",
-    "    plan_output += f'{manager.IndexToNode(index)}\\n'\n",
-    "    plan_output += f'Distance of the route: {route_distance}m\\n'\n",
+    "    plan_output += f\"{manager.IndexToNode(index)}\\n\"\n",
+    "    plan_output += f\"Distance of the route: {route_distance}m\\n\"\n",
     "    print(plan_output)\n",
     "\n",
     "\n",
diff --git a/examples/notebook/constraint_solver/tsp.ipynb b/examples/notebook/constraint_solver/tsp.ipynb
index 402d20ff1b..3ee35985f9 100644
--- a/examples/notebook/constraint_solver/tsp.ipynb
+++ b/examples/notebook/constraint_solver/tsp.ipynb
@@ -95,20 +95,23 @@
     "    \"\"\"Stores the data for the problem.\"\"\"\n",
     "    data = {}\n",
     "    # Locations in block units\n",
-    "    locations = \\\n",
-    "            [(4, 4),  # depot\n",
-    "             (2, 0), (8, 0),  # locations to visit\n",
-    "             (0, 1), (1, 1),\n",
-    "             (5, 2), (7, 2),\n",
-    "             (3, 3), (6, 3),\n",
-    "             (5, 5), (8, 5),\n",
-    "             (1, 6), (2, 6),\n",
-    "             (3, 7), (6, 7),\n",
-    "             (0, 8), (7, 8),]\n",
+    "    locations = [\n",
+    "        # fmt:off\n",
+    "      (4, 4),  # depot\n",
+    "      (2, 0), (8, 0),  # locations to visit\n",
+    "      (0, 1), (1, 1),\n",
+    "      (5, 2), (7, 2),\n",
+    "      (3, 3), (6, 3),\n",
+    "      (5, 5), (8, 5),\n",
+    "      (1, 6), (2, 6),\n",
+    "      (3, 7), (6, 7),\n",
+    "      (0, 8), (7, 8)\n",
+    "        # fmt:on\n",
+    "    ]\n",
     "    # Convert locations in meters using a city block dimension of 114m x 80m.\n",
-    "    data['locations'] = [(l[0] * 114, l[1] * 80) for l in locations]\n",
-    "    data['num_vehicles'] = 1\n",
-    "    data['depot'] = 0\n",
+    "    data[\"locations\"] = [(l[0] * 114, l[1] * 80) for l in locations]\n",
+    "    data[\"num_vehicles\"] = 1\n",
+    "    data[\"depot\"] = 0\n",
     "    return data\n",
     "\n",
     "\n",
@@ -117,15 +120,15 @@
     "    distances_ = {}\n",
     "    index_manager_ = manager\n",
     "    # precompute distance between location to have distance callback in O(1)\n",
-    "    for from_counter, from_node in enumerate(data['locations']):\n",
+    "    for from_counter, from_node in enumerate(data[\"locations\"]):\n",
     "        distances_[from_counter] = {}\n",
-    "        for to_counter, to_node in enumerate(data['locations']):\n",
+    "        for to_counter, to_node in enumerate(data[\"locations\"]):\n",
     "            if from_counter == to_counter:\n",
     "                distances_[from_counter][to_counter] = 0\n",
     "            else:\n",
-    "                distances_[from_counter][to_counter] = (\n",
-    "                    abs(from_node[0] - to_node[0]) +\n",
-    "                    abs(from_node[1] - to_node[1]))\n",
+    "                distances_[from_counter][to_counter] = abs(\n",
+    "                    from_node[0] - to_node[0]\n",
+    "                ) + abs(from_node[1] - to_node[1])\n",
     "\n",
     "    def distance_callback(from_index, to_index):\n",
     "        \"\"\"Returns the manhattan distance between the two nodes.\"\"\"\n",
@@ -139,17 +142,17 @@
     "\n",
     "def print_solution(manager, routing, assignment):\n",
     "    \"\"\"Prints assignment on console.\"\"\"\n",
-    "    print(f'Objective: {assignment.ObjectiveValue()}')\n",
+    "    print(f\"Objective: {assignment.ObjectiveValue()}\")\n",
     "    index = routing.Start(0)\n",
-    "    plan_output = 'Route for vehicle 0:\\n'\n",
+    "    plan_output = \"Route for vehicle 0:\\n\"\n",
     "    route_distance = 0\n",
     "    while not routing.IsEnd(index):\n",
-    "        plan_output += f' {manager.IndexToNode(index)} ->'\n",
+    "        plan_output += f\" {manager.IndexToNode(index)} ->\"\n",
     "        previous_index = index\n",
     "        index = assignment.Value(routing.NextVar(index))\n",
     "        route_distance += routing.GetArcCostForVehicle(previous_index, index, 0)\n",
-    "    plan_output += f' {manager.IndexToNode(index)}\\n'\n",
-    "    plan_output += f'Distance of the route: {route_distance}m\\n'\n",
+    "    plan_output += f\" {manager.IndexToNode(index)}\\n\"\n",
+    "    plan_output += f\"Distance of the route: {route_distance}m\\n\"\n",
     "    print(plan_output)\n",
     "\n",
     "\n",
@@ -159,8 +162,9 @@
     "    data = create_data_model()\n",
     "\n",
     "    # Create the routing index manager.\n",
-    "    manager = pywrapcp.RoutingIndexManager(len(data['locations']),\n",
-    "                                           data['num_vehicles'], data['depot'])\n",
+    "    manager = pywrapcp.RoutingIndexManager(\n",
+    "        len(data[\"locations\"]), data[\"num_vehicles\"], data[\"depot\"]\n",
+    "    )\n",
     "\n",
     "    # Create Routing Model.\n",
     "    routing = pywrapcp.RoutingModel(manager)\n",
@@ -175,7 +179,8 @@
     "    # Setting first solution heuristic.\n",
     "    search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n",
     "    search_parameters.first_solution_strategy = (\n",
-    "        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)\n",
+    "        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC\n",
+    "    )\n",
     "\n",
     "    # Solve the problem.\n",
     "    assignment = routing.SolveWithParameters(search_parameters)\n",
diff --git a/examples/notebook/constraint_solver/tsp_circuit_board.ipynb b/examples/notebook/constraint_solver/tsp_circuit_board.ipynb
index 94217052c1..0f82a14bf2 100644
--- a/examples/notebook/constraint_solver/tsp_circuit_board.ipynb
+++ b/examples/notebook/constraint_solver/tsp_circuit_board.ipynb
@@ -92,57 +92,59 @@
     "    \"\"\"Stores the data for the problem.\"\"\"\n",
     "    data = {}\n",
     "    # Locations in block units\n",
-    "    data['locations'] = [\n",
-    "        (288, 149), (288, 129), (270, 133), (256, 141), (256, 157), (246, 157),\n",
-    "        (236, 169), (228, 169), (228, 161), (220, 169), (212, 169), (204, 169),\n",
-    "        (196, 169), (188, 169), (196, 161), (188, 145), (172, 145), (164, 145),\n",
-    "        (156, 145), (148, 145), (140, 145), (148, 169), (164, 169), (172, 169),\n",
-    "        (156, 169), (140, 169), (132, 169), (124, 169), (116, 161), (104, 153),\n",
-    "        (104, 161), (104, 169), (90, 165), (80, 157), (64, 157), (64, 165),\n",
-    "        (56, 169), (56, 161), (56, 153), (56, 145), (56, 137), (56, 129),\n",
-    "        (56, 121), (40, 121), (40, 129), (40, 137), (40, 145), (40, 153),\n",
-    "        (40, 161), (40, 169), (32, 169), (32, 161), (32, 153), (32, 145),\n",
-    "        (32, 137), (32, 129), (32, 121), (32, 113), (40, 113), (56, 113),\n",
-    "        (56, 105), (48, 99), (40, 99), (32, 97), (32, 89), (24, 89),\n",
-    "        (16, 97), (16, 109), (8, 109), (8, 97), (8, 89), (8, 81),\n",
-    "        (8, 73), (8, 65), (8, 57), (16, 57), (8, 49), (8, 41),\n",
-    "        (24, 45), (32, 41), (32, 49), (32, 57), (32, 65), (32, 73),\n",
-    "        (32, 81), (40, 83), (40, 73), (40, 63), (40, 51), (44, 43),\n",
-    "        (44, 35), (44, 27), (32, 25), (24, 25), (16, 25), (16, 17),\n",
-    "        (24, 17), (32, 17), (44, 11), (56, 9), (56, 17), (56, 25),\n",
-    "        (56, 33), (56, 41), (64, 41), (72, 41), (72, 49), (56, 49),\n",
-    "        (48, 51), (56, 57), (56, 65), (48, 63), (48, 73), (56, 73),\n",
-    "        (56, 81), (48, 83), (56, 89), (56, 97), (104, 97), (104, 105),\n",
-    "        (104, 113), (104, 121), (104, 129), (104, 137), (104, 145), (116, 145),\n",
-    "        (124, 145), (132, 145), (132, 137), (140, 137), (148, 137), (156, 137),\n",
-    "        (164, 137), (172, 125), (172, 117), (172, 109), (172, 101), (172, 93),\n",
-    "        (172, 85), (180, 85), (180, 77), (180, 69), (180, 61), (180, 53),\n",
-    "        (172, 53), (172, 61), (172, 69), (172, 77), (164, 81), (148, 85),\n",
-    "        (124, 85), (124, 93), (124, 109), (124, 125), (124, 117), (124, 101),\n",
-    "        (104, 89), (104, 81), (104, 73), (104, 65), (104, 49), (104, 41),\n",
-    "        (104, 33), (104, 25), (104, 17), (92, 9), (80, 9), (72, 9),\n",
-    "        (64, 21), (72, 25), (80, 25), (80, 25), (80, 41), (88, 49),\n",
-    "        (104, 57), (124, 69), (124, 77), (132, 81), (140, 65), (132, 61),\n",
-    "        (124, 61), (124, 53), (124, 45), (124, 37), (124, 29), (132, 21),\n",
-    "        (124, 21), (120, 9), (128, 9), (136, 9), (148, 9), (162, 9),\n",
-    "        (156, 25), (172, 21), (180, 21), (180, 29), (172, 29), (172, 37),\n",
-    "        (172, 45), (180, 45), (180, 37), (188, 41), (196, 49), (204, 57),\n",
-    "        (212, 65), (220, 73), (228, 69), (228, 77), (236, 77), (236, 69),\n",
-    "        (236, 61), (228, 61), (228, 53), (236, 53), (236, 45), (228, 45),\n",
-    "        (228, 37), (236, 37), (236, 29), (228, 29), (228, 21), (236, 21),\n",
-    "        (252, 21), (260, 29), (260, 37), (260, 45), (260, 53), (260, 61),\n",
-    "        (260, 69), (260, 77), (276, 77), (276, 69), (276, 61), (276, 53),\n",
-    "        (284, 53), (284, 61), (284, 69), (284, 77), (284, 85), (284, 93),\n",
-    "        (284, 101), (288, 109), (280, 109), (276, 101), (276, 93), (276, 85),\n",
-    "        (268, 97), (260, 109), (252, 101), (260, 93), (260, 85), (236, 85),\n",
-    "        (228, 85), (228, 93), (236, 93), (236, 101), (228, 101), (228, 109),\n",
-    "        (228, 117), (228, 125), (220, 125), (212, 117), (204, 109), (196, 101),\n",
-    "        (188, 93), (180, 93), (180, 101), (180, 109), (180, 117), (180, 125),\n",
-    "        (196, 145), (204, 145), (212, 145), (220, 145), (228, 145), (236, 145),\n",
-    "        (246, 141), (252, 125), (260, 129), (280, 133)\n",
-    "    ]  # yapf: disable\n",
-    "    data['num_vehicles'] = 1\n",
-    "    data['depot'] = 0\n",
+    "    data[\"locations\"] = [\n",
+    "        # fmt: off\n",
+    "      (288, 149), (288, 129), (270, 133), (256, 141), (256, 157), (246, 157),\n",
+    "      (236, 169), (228, 169), (228, 161), (220, 169), (212, 169), (204, 169),\n",
+    "      (196, 169), (188, 169), (196, 161), (188, 145), (172, 145), (164, 145),\n",
+    "      (156, 145), (148, 145), (140, 145), (148, 169), (164, 169), (172, 169),\n",
+    "      (156, 169), (140, 169), (132, 169), (124, 169), (116, 161), (104, 153),\n",
+    "      (104, 161), (104, 169), (90, 165), (80, 157), (64, 157), (64, 165),\n",
+    "      (56, 169), (56, 161), (56, 153), (56, 145), (56, 137), (56, 129),\n",
+    "      (56, 121), (40, 121), (40, 129), (40, 137), (40, 145), (40, 153),\n",
+    "      (40, 161), (40, 169), (32, 169), (32, 161), (32, 153), (32, 145),\n",
+    "      (32, 137), (32, 129), (32, 121), (32, 113), (40, 113), (56, 113),\n",
+    "      (56, 105), (48, 99), (40, 99), (32, 97), (32, 89), (24, 89),\n",
+    "      (16, 97), (16, 109), (8, 109), (8, 97), (8, 89), (8, 81),\n",
+    "      (8, 73), (8, 65), (8, 57), (16, 57), (8, 49), (8, 41),\n",
+    "      (24, 45), (32, 41), (32, 49), (32, 57), (32, 65), (32, 73),\n",
+    "      (32, 81), (40, 83), (40, 73), (40, 63), (40, 51), (44, 43),\n",
+    "      (44, 35), (44, 27), (32, 25), (24, 25), (16, 25), (16, 17),\n",
+    "      (24, 17), (32, 17), (44, 11), (56, 9), (56, 17), (56, 25),\n",
+    "      (56, 33), (56, 41), (64, 41), (72, 41), (72, 49), (56, 49),\n",
+    "      (48, 51), (56, 57), (56, 65), (48, 63), (48, 73), (56, 73),\n",
+    "      (56, 81), (48, 83), (56, 89), (56, 97), (104, 97), (104, 105),\n",
+    "      (104, 113), (104, 121), (104, 129), (104, 137), (104, 145), (116, 145),\n",
+    "      (124, 145), (132, 145), (132, 137), (140, 137), (148, 137), (156, 137),\n",
+    "      (164, 137), (172, 125), (172, 117), (172, 109), (172, 101), (172, 93),\n",
+    "      (172, 85), (180, 85), (180, 77), (180, 69), (180, 61), (180, 53),\n",
+    "      (172, 53), (172, 61), (172, 69), (172, 77), (164, 81), (148, 85),\n",
+    "      (124, 85), (124, 93), (124, 109), (124, 125), (124, 117), (124, 101),\n",
+    "      (104, 89), (104, 81), (104, 73), (104, 65), (104, 49), (104, 41),\n",
+    "      (104, 33), (104, 25), (104, 17), (92, 9), (80, 9), (72, 9),\n",
+    "      (64, 21), (72, 25), (80, 25), (80, 25), (80, 41), (88, 49),\n",
+    "      (104, 57), (124, 69), (124, 77), (132, 81), (140, 65), (132, 61),\n",
+    "      (124, 61), (124, 53), (124, 45), (124, 37), (124, 29), (132, 21),\n",
+    "      (124, 21), (120, 9), (128, 9), (136, 9), (148, 9), (162, 9),\n",
+    "      (156, 25), (172, 21), (180, 21), (180, 29), (172, 29), (172, 37),\n",
+    "      (172, 45), (180, 45), (180, 37), (188, 41), (196, 49), (204, 57),\n",
+    "      (212, 65), (220, 73), (228, 69), (228, 77), (236, 77), (236, 69),\n",
+    "      (236, 61), (228, 61), (228, 53), (236, 53), (236, 45), (228, 45),\n",
+    "      (228, 37), (236, 37), (236, 29), (228, 29), (228, 21), (236, 21),\n",
+    "      (252, 21), (260, 29), (260, 37), (260, 45), (260, 53), (260, 61),\n",
+    "      (260, 69), (260, 77), (276, 77), (276, 69), (276, 61), (276, 53),\n",
+    "      (284, 53), (284, 61), (284, 69), (284, 77), (284, 85), (284, 93),\n",
+    "      (284, 101), (288, 109), (280, 109), (276, 101), (276, 93), (276, 85),\n",
+    "      (268, 97), (260, 109), (252, 101), (260, 93), (260, 85), (236, 85),\n",
+    "      (228, 85), (228, 93), (236, 93), (236, 101), (228, 101), (228, 109),\n",
+    "      (228, 117), (228, 125), (220, 125), (212, 117), (204, 109), (196, 101),\n",
+    "      (188, 93), (180, 93), (180, 101), (180, 109), (180, 117), (180, 125),\n",
+    "      (196, 145), (204, 145), (212, 145), (220, 145), (228, 145), (236, 145),\n",
+    "      (246, 141), (252, 125), (260, 129), (280, 133)\n",
+    "        # fmt: on\n",
+    "    ]\n",
+    "    data[\"num_vehicles\"] = 1\n",
+    "    data[\"depot\"] = 0\n",
     "    return data\n",
     "\n",
     "\n",
@@ -156,26 +158,26 @@
     "                distances[from_counter][to_counter] = 0\n",
     "            else:\n",
     "                # Euclidean distance\n",
-    "                distances[from_counter][to_counter] = (int(\n",
-    "                    math.hypot((from_node[0] - to_node[0]),\n",
-    "                               (from_node[1] - to_node[1]))))\n",
+    "                distances[from_counter][to_counter] = int(\n",
+    "                    math.hypot((from_node[0] - to_node[0]), (from_node[1] - to_node[1]))\n",
+    "                )\n",
     "    return distances\n",
     "\n",
     "\n",
     "def print_solution(manager, routing, solution):\n",
     "    \"\"\"Prints solution on console.\"\"\"\n",
-    "    print(f'Objective: {solution.ObjectiveValue()}')\n",
+    "    print(f\"Objective: {solution.ObjectiveValue()}\")\n",
     "    index = routing.Start(0)\n",
-    "    plan_output = 'Route:\\n'\n",
+    "    plan_output = \"Route:\\n\"\n",
     "    route_distance = 0\n",
     "    while not routing.IsEnd(index):\n",
-    "        plan_output += f' {manager.IndexToNode(index)} ->'\n",
+    "        plan_output += f\" {manager.IndexToNode(index)} ->\"\n",
     "        previous_index = index\n",
     "        index = solution.Value(routing.NextVar(index))\n",
     "        route_distance += routing.GetArcCostForVehicle(previous_index, index, 0)\n",
-    "    plan_output += f' {manager.IndexToNode(index)}\\n'\n",
+    "    plan_output += f\" {manager.IndexToNode(index)}\\n\"\n",
     "    print(plan_output)\n",
-    "    plan_output += f'Objective: {route_distance}m\\n'\n",
+    "    plan_output += f\"Objective: {route_distance}m\\n\"\n",
     "\n",
     "\n",
     "def main():\n",
@@ -184,13 +186,14 @@
     "    data = create_data_model()\n",
     "\n",
     "    # Create the routing index manager.\n",
-    "    manager = pywrapcp.RoutingIndexManager(len(data['locations']),\n",
-    "                                           data['num_vehicles'], data['depot'])\n",
+    "    manager = pywrapcp.RoutingIndexManager(\n",
+    "        len(data[\"locations\"]), data[\"num_vehicles\"], data[\"depot\"]\n",
+    "    )\n",
     "\n",
     "    # Create Routing Model.\n",
     "    routing = pywrapcp.RoutingModel(manager)\n",
     "\n",
-    "    distance_matrix = compute_euclidean_distance_matrix(data['locations'])\n",
+    "    distance_matrix = compute_euclidean_distance_matrix(data[\"locations\"])\n",
     "\n",
     "    def distance_callback(from_index, to_index):\n",
     "        \"\"\"Returns the distance between the two nodes.\"\"\"\n",
@@ -207,7 +210,8 @@
     "    # Setting first solution heuristic.\n",
     "    search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n",
     "    search_parameters.first_solution_strategy = (\n",
-    "        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)\n",
+    "        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC\n",
+    "    )\n",
     "\n",
     "    # Solve the problem.\n",
     "    solution = routing.SolveWithParameters(search_parameters)\n",
diff --git a/examples/notebook/constraint_solver/tsp_cities.ipynb b/examples/notebook/constraint_solver/tsp_cities.ipynb
index 1b683833af..84ba66d898 100644
--- a/examples/notebook/constraint_solver/tsp_cities.ipynb
+++ b/examples/notebook/constraint_solver/tsp_cities.ipynb
@@ -90,7 +90,7 @@
     "def create_data_model():\n",
     "    \"\"\"Stores the data for the problem.\"\"\"\n",
     "    data = {}\n",
-    "    data['distance_matrix'] = [\n",
+    "    data[\"distance_matrix\"] = [\n",
     "        [0, 2451, 713, 1018, 1631, 1374, 2408, 213, 2571, 875, 1420, 2145, 1972],\n",
     "        [2451, 0, 1745, 1524, 831, 1240, 959, 2596, 403, 1589, 1374, 357, 579],\n",
     "        [713, 1745, 0, 355, 920, 803, 1737, 851, 1858, 262, 940, 1453, 1260],\n",
@@ -104,26 +104,26 @@
     "        [1420, 1374, 940, 1056, 879, 225, 1891, 1605, 1645, 679, 0, 1017, 1200],\n",
     "        [2145, 357, 1453, 1280, 586, 887, 1114, 2300, 653, 1272, 1017, 0, 504],\n",
     "        [1972, 579, 1260, 987, 371, 999, 701, 2099, 600, 1162, 1200, 504, 0],\n",
-    "    ]  # yapf: disable\n",
-    "    data['num_vehicles'] = 1\n",
-    "    data['depot'] = 0\n",
+    "    ]\n",
+    "    data[\"num_vehicles\"] = 1\n",
+    "    data[\"depot\"] = 0\n",
     "    return data\n",
     "\n",
     "\n",
     "def print_solution(manager, routing, solution):\n",
     "    \"\"\"Prints solution on console.\"\"\"\n",
-    "    print(f'Objective: {solution.ObjectiveValue()} miles')\n",
+    "    print(f\"Objective: {solution.ObjectiveValue()} miles\")\n",
     "    index = routing.Start(0)\n",
-    "    plan_output = 'Route for vehicle 0:\\n'\n",
+    "    plan_output = \"Route for vehicle 0:\\n\"\n",
     "    route_distance = 0\n",
     "    while not routing.IsEnd(index):\n",
-    "        plan_output += f' {manager.IndexToNode(index)} ->'\n",
+    "        plan_output += f\" {manager.IndexToNode(index)} ->\"\n",
     "        previous_index = index\n",
     "        index = solution.Value(routing.NextVar(index))\n",
     "        route_distance += routing.GetArcCostForVehicle(previous_index, index, 0)\n",
-    "    plan_output += f' {manager.IndexToNode(index)}\\n'\n",
+    "    plan_output += f\" {manager.IndexToNode(index)}\\n\"\n",
     "    print(plan_output)\n",
-    "    plan_output += f'Route distance: {route_distance}miles\\n'\n",
+    "    plan_output += f\"Route distance: {route_distance}miles\\n\"\n",
     "\n",
     "\n",
     "def main():\n",
@@ -132,8 +132,9 @@
     "    data = create_data_model()\n",
     "\n",
     "    # Create the routing index manager.\n",
-    "    manager = pywrapcp.RoutingIndexManager(len(data['distance_matrix']),\n",
-    "                                           data['num_vehicles'], data['depot'])\n",
+    "    manager = pywrapcp.RoutingIndexManager(\n",
+    "        len(data[\"distance_matrix\"]), data[\"num_vehicles\"], data[\"depot\"]\n",
+    "    )\n",
     "\n",
     "    # Create Routing Model.\n",
     "    routing = pywrapcp.RoutingModel(manager)\n",
@@ -144,7 +145,7 @@
     "        # Convert from routing variable Index to distance matrix NodeIndex.\n",
     "        from_node = manager.IndexToNode(from_index)\n",
     "        to_node = manager.IndexToNode(to_index)\n",
-    "        return data['distance_matrix'][from_node][to_node]\n",
+    "        return data[\"distance_matrix\"][from_node][to_node]\n",
     "\n",
     "    transit_callback_index = routing.RegisterTransitCallback(distance_callback)\n",
     "\n",
@@ -154,7 +155,8 @@
     "    # Setting first solution heuristic.\n",
     "    search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n",
     "    search_parameters.first_solution_strategy = (\n",
-    "        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)\n",
+    "        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC\n",
+    "    )\n",
     "\n",
     "    # Solve the problem.\n",
     "    solution = routing.SolveWithParameters(search_parameters)\n",
diff --git a/examples/notebook/constraint_solver/tsp_distance_matrix.ipynb b/examples/notebook/constraint_solver/tsp_distance_matrix.ipynb
index dcda4855b0..623296389a 100644
--- a/examples/notebook/constraint_solver/tsp_distance_matrix.ipynb
+++ b/examples/notebook/constraint_solver/tsp_distance_matrix.ipynb
@@ -90,94 +90,45 @@
     "def create_data_model():\n",
     "    \"\"\"Stores the data for the problem.\"\"\"\n",
     "    data = {}\n",
-    "    data['distance_matrix'] = [\n",
-    "        [\n",
-    "            0, 548, 776, 696, 582, 274, 502, 194, 308, 194, 536, 502, 388, 354,\n",
-    "            468, 776, 662\n",
-    "        ],\n",
-    "        [\n",
-    "            548, 0, 684, 308, 194, 502, 730, 354, 696, 742, 1084, 594, 480, 674,\n",
-    "            1016, 868, 1210\n",
-    "        ],\n",
-    "        [\n",
-    "            776, 684, 0, 992, 878, 502, 274, 810, 468, 742, 400, 1278, 1164,\n",
-    "            1130, 788, 1552, 754\n",
-    "        ],\n",
-    "        [\n",
-    "            696, 308, 992, 0, 114, 650, 878, 502, 844, 890, 1232, 514, 628, 822,\n",
-    "            1164, 560, 1358\n",
-    "        ],\n",
-    "        [\n",
-    "            582, 194, 878, 114, 0, 536, 764, 388, 730, 776, 1118, 400, 514, 708,\n",
-    "            1050, 674, 1244\n",
-    "        ],\n",
-    "        [\n",
-    "            274, 502, 502, 650, 536, 0, 228, 308, 194, 240, 582, 776, 662, 628,\n",
-    "            514, 1050, 708\n",
-    "        ],\n",
-    "        [\n",
-    "            502, 730, 274, 878, 764, 228, 0, 536, 194, 468, 354, 1004, 890, 856,\n",
-    "            514, 1278, 480\n",
-    "        ],\n",
-    "        [\n",
-    "            194, 354, 810, 502, 388, 308, 536, 0, 342, 388, 730, 468, 354, 320,\n",
-    "            662, 742, 856\n",
-    "        ],\n",
-    "        [\n",
-    "            308, 696, 468, 844, 730, 194, 194, 342, 0, 274, 388, 810, 696, 662,\n",
-    "            320, 1084, 514\n",
-    "        ],\n",
-    "        [\n",
-    "            194, 742, 742, 890, 776, 240, 468, 388, 274, 0, 342, 536, 422, 388,\n",
-    "            274, 810, 468\n",
-    "        ],\n",
-    "        [\n",
-    "            536, 1084, 400, 1232, 1118, 582, 354, 730, 388, 342, 0, 878, 764,\n",
-    "            730, 388, 1152, 354\n",
-    "        ],\n",
-    "        [\n",
-    "            502, 594, 1278, 514, 400, 776, 1004, 468, 810, 536, 878, 0, 114,\n",
-    "            308, 650, 274, 844\n",
-    "        ],\n",
-    "        [\n",
-    "            388, 480, 1164, 628, 514, 662, 890, 354, 696, 422, 764, 114, 0, 194,\n",
-    "            536, 388, 730\n",
-    "        ],\n",
-    "        [\n",
-    "            354, 674, 1130, 822, 708, 628, 856, 320, 662, 388, 730, 308, 194, 0,\n",
-    "            342, 422, 536\n",
-    "        ],\n",
-    "        [\n",
-    "            468, 1016, 788, 1164, 1050, 514, 514, 662, 320, 274, 388, 650, 536,\n",
-    "            342, 0, 764, 194\n",
-    "        ],\n",
-    "        [\n",
-    "            776, 868, 1552, 560, 674, 1050, 1278, 742, 1084, 810, 1152, 274,\n",
-    "            388, 422, 764, 0, 798\n",
-    "        ],\n",
-    "        [\n",
-    "            662, 1210, 754, 1358, 1244, 708, 480, 856, 514, 468, 354, 844, 730,\n",
-    "            536, 194, 798, 0\n",
-    "        ],\n",
+    "    data[\"distance_matrix\"] = [\n",
+    "        # fmt: off\n",
+    "      [0, 548, 776, 696, 582, 274, 502, 194, 308, 194, 536, 502, 388, 354, 468, 776, 662],\n",
+    "      [548, 0, 684, 308, 194, 502, 730, 354, 696, 742, 1084, 594, 480, 674, 1016, 868, 1210],\n",
+    "      [776, 684, 0, 992, 878, 502, 274, 810, 468, 742, 400, 1278, 1164, 1130, 788, 1552, 754],\n",
+    "      [696, 308, 992, 0, 114, 650, 878, 502, 844, 890, 1232, 514, 628, 822, 1164, 560, 1358],\n",
+    "      [582, 194, 878, 114, 0, 536, 764, 388, 730, 776, 1118, 400, 514, 708, 1050, 674, 1244],\n",
+    "      [274, 502, 502, 650, 536, 0, 228, 308, 194, 240, 582, 776, 662, 628, 514, 1050, 708],\n",
+    "      [502, 730, 274, 878, 764, 228, 0, 536, 194, 468, 354, 1004, 890, 856, 514, 1278, 480],\n",
+    "      [194, 354, 810, 502, 388, 308, 536, 0, 342, 388, 730, 468, 354, 320, 662, 742, 856],\n",
+    "      [308, 696, 468, 844, 730, 194, 194, 342, 0, 274, 388, 810, 696, 662, 320, 1084, 514],\n",
+    "      [194, 742, 742, 890, 776, 240, 468, 388, 274, 0, 342, 536, 422, 388, 274, 810, 468],\n",
+    "      [536, 1084, 400, 1232, 1118, 582, 354, 730, 388, 342, 0, 878, 764, 730, 388, 1152, 354],\n",
+    "      [502, 594, 1278, 514, 400, 776, 1004, 468, 810, 536, 878, 0, 114, 308, 650, 274, 844],\n",
+    "      [388, 480, 1164, 628, 514, 662, 890, 354, 696, 422, 764, 114, 0, 194, 536, 388, 730],\n",
+    "      [354, 674, 1130, 822, 708, 628, 856, 320, 662, 388, 730, 308, 194, 0, 342, 422, 536],\n",
+    "      [468, 1016, 788, 1164, 1050, 514, 514, 662, 320, 274, 388, 650, 536, 342, 0, 764, 194],\n",
+    "      [776, 868, 1552, 560, 674, 1050, 1278, 742, 1084, 810, 1152, 274, 388, 422, 764, 0, 798],\n",
+    "      [662, 1210, 754, 1358, 1244, 708, 480, 856, 514, 468, 354, 844, 730, 536, 194, 798, 0],\n",
+    "        # fmt: on\n",
     "    ]\n",
-    "    data['num_vehicles'] = 1\n",
-    "    data['depot'] = 0\n",
+    "    data[\"num_vehicles\"] = 1\n",
+    "    data[\"depot\"] = 0\n",
     "    return data\n",
     "\n",
     "\n",
     "def print_solution(manager, routing, solution):\n",
     "    \"\"\"Prints solution on console.\"\"\"\n",
-    "    print(f'Objective: {solution.ObjectiveValue()}')\n",
+    "    print(f\"Objective: {solution.ObjectiveValue()}\")\n",
     "    index = routing.Start(0)\n",
-    "    plan_output = 'Route for vehicle 0:\\n'\n",
+    "    plan_output = \"Route for vehicle 0:\\n\"\n",
     "    route_distance = 0\n",
     "    while not routing.IsEnd(index):\n",
-    "        plan_output += f' {manager.IndexToNode(index)} ->'\n",
+    "        plan_output += f\" {manager.IndexToNode(index)} ->\"\n",
     "        previous_index = index\n",
     "        index = solution.Value(routing.NextVar(index))\n",
     "        route_distance += routing.GetArcCostForVehicle(previous_index, index, 0)\n",
-    "    plan_output += f' {manager.IndexToNode(index)}\\n'\n",
-    "    plan_output += f'Distance of the route: {route_distance}m\\n'\n",
+    "    plan_output += f\" {manager.IndexToNode(index)}\\n\"\n",
+    "    plan_output += f\"Distance of the route: {route_distance}m\\n\"\n",
     "    print(plan_output)\n",
     "\n",
     "\n",
@@ -187,20 +138,20 @@
     "    data = create_data_model()\n",
     "\n",
     "    # Create the routing index manager.\n",
-    "    manager = pywrapcp.RoutingIndexManager(len(data['distance_matrix']),\n",
-    "                                           data['num_vehicles'], data['depot'])\n",
+    "    manager = pywrapcp.RoutingIndexManager(\n",
+    "        len(data[\"distance_matrix\"]), data[\"num_vehicles\"], data[\"depot\"]\n",
+    "    )\n",
     "\n",
     "    # Create Routing Model.\n",
     "    routing = pywrapcp.RoutingModel(manager)\n",
     "\n",
-    "\n",
     "    # Create and register a transit callback.\n",
     "    def distance_callback(from_index, to_index):\n",
     "        \"\"\"Returns the distance between the two nodes.\"\"\"\n",
     "        # Convert from routing variable Index to distance matrix NodeIndex.\n",
     "        from_node = manager.IndexToNode(from_index)\n",
     "        to_node = manager.IndexToNode(to_index)\n",
-    "        return data['distance_matrix'][from_node][to_node]\n",
+    "        return data[\"distance_matrix\"][from_node][to_node]\n",
     "\n",
     "    transit_callback_index = routing.RegisterTransitCallback(distance_callback)\n",
     "\n",
@@ -210,7 +161,8 @@
     "    # Setting first solution heuristic.\n",
     "    search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n",
     "    search_parameters.first_solution_strategy = (\n",
-    "        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)\n",
+    "        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC\n",
+    "    )\n",
     "\n",
     "    # Solve the problem.\n",
     "    solution = routing.SolveWithParameters(search_parameters)\n",
diff --git a/examples/notebook/constraint_solver/vrp.ipynb b/examples/notebook/constraint_solver/vrp.ipynb
index 7fa7123810..c656f9c065 100644
--- a/examples/notebook/constraint_solver/vrp.ipynb
+++ b/examples/notebook/constraint_solver/vrp.ipynb
@@ -98,100 +98,52 @@
     "def create_data_model():\n",
     "    \"\"\"Stores the data for the problem.\"\"\"\n",
     "    data = {}\n",
-    "    data['distance_matrix'] = [\n",
-    "        [\n",
-    "            0, 548, 776, 696, 582, 274, 502, 194, 308, 194, 536, 502, 388, 354,\n",
-    "            468, 776, 662\n",
-    "        ],\n",
-    "        [\n",
-    "            548, 0, 684, 308, 194, 502, 730, 354, 696, 742, 1084, 594, 480, 674,\n",
-    "            1016, 868, 1210\n",
-    "        ],\n",
-    "        [\n",
-    "            776, 684, 0, 992, 878, 502, 274, 810, 468, 742, 400, 1278, 1164,\n",
-    "            1130, 788, 1552, 754\n",
-    "        ],\n",
-    "        [\n",
-    "            696, 308, 992, 0, 114, 650, 878, 502, 844, 890, 1232, 514, 628, 822,\n",
-    "            1164, 560, 1358\n",
-    "        ],\n",
-    "        [\n",
-    "            582, 194, 878, 114, 0, 536, 764, 388, 730, 776, 1118, 400, 514, 708,\n",
-    "            1050, 674, 1244\n",
-    "        ],\n",
-    "        [\n",
-    "            274, 502, 502, 650, 536, 0, 228, 308, 194, 240, 582, 776, 662, 628,\n",
-    "            514, 1050, 708\n",
-    "        ],\n",
-    "        [\n",
-    "            502, 730, 274, 878, 764, 228, 0, 536, 194, 468, 354, 1004, 890, 856,\n",
-    "            514, 1278, 480\n",
-    "        ],\n",
-    "        [\n",
-    "            194, 354, 810, 502, 388, 308, 536, 0, 342, 388, 730, 468, 354, 320,\n",
-    "            662, 742, 856\n",
-    "        ],\n",
-    "        [\n",
-    "            308, 696, 468, 844, 730, 194, 194, 342, 0, 274, 388, 810, 696, 662,\n",
-    "            320, 1084, 514\n",
-    "        ],\n",
-    "        [\n",
-    "            194, 742, 742, 890, 776, 240, 468, 388, 274, 0, 342, 536, 422, 388,\n",
-    "            274, 810, 468\n",
-    "        ],\n",
-    "        [\n",
-    "            536, 1084, 400, 1232, 1118, 582, 354, 730, 388, 342, 0, 878, 764,\n",
-    "            730, 388, 1152, 354\n",
-    "        ],\n",
-    "        [\n",
-    "            502, 594, 1278, 514, 400, 776, 1004, 468, 810, 536, 878, 0, 114,\n",
-    "            308, 650, 274, 844\n",
-    "        ],\n",
-    "        [\n",
-    "            388, 480, 1164, 628, 514, 662, 890, 354, 696, 422, 764, 114, 0, 194,\n",
-    "            536, 388, 730\n",
-    "        ],\n",
-    "        [\n",
-    "            354, 674, 1130, 822, 708, 628, 856, 320, 662, 388, 730, 308, 194, 0,\n",
-    "            342, 422, 536\n",
-    "        ],\n",
-    "        [\n",
-    "            468, 1016, 788, 1164, 1050, 514, 514, 662, 320, 274, 388, 650, 536,\n",
-    "            342, 0, 764, 194\n",
-    "        ],\n",
-    "        [\n",
-    "            776, 868, 1552, 560, 674, 1050, 1278, 742, 1084, 810, 1152, 274,\n",
-    "            388, 422, 764, 0, 798\n",
-    "        ],\n",
-    "        [\n",
-    "            662, 1210, 754, 1358, 1244, 708, 480, 856, 514, 468, 354, 844, 730,\n",
-    "            536, 194, 798, 0\n",
-    "        ],\n",
+    "    data[\"distance_matrix\"] = [\n",
+    "        # fmt: off\n",
+    "      [0, 548, 776, 696, 582, 274, 502, 194, 308, 194, 536, 502, 388, 354, 468, 776, 662],\n",
+    "      [548, 0, 684, 308, 194, 502, 730, 354, 696, 742, 1084, 594, 480, 674, 1016, 868, 1210],\n",
+    "      [776, 684, 0, 992, 878, 502, 274, 810, 468, 742, 400, 1278, 1164, 1130, 788, 1552, 754],\n",
+    "      [696, 308, 992, 0, 114, 650, 878, 502, 844, 890, 1232, 514, 628, 822, 1164, 560, 1358],\n",
+    "      [582, 194, 878, 114, 0, 536, 764, 388, 730, 776, 1118, 400, 514, 708, 1050, 674, 1244],\n",
+    "      [274, 502, 502, 650, 536, 0, 228, 308, 194, 240, 582, 776, 662, 628, 514, 1050, 708],\n",
+    "      [502, 730, 274, 878, 764, 228, 0, 536, 194, 468, 354, 1004, 890, 856, 514, 1278, 480],\n",
+    "      [194, 354, 810, 502, 388, 308, 536, 0, 342, 388, 730, 468, 354, 320, 662, 742, 856],\n",
+    "      [308, 696, 468, 844, 730, 194, 194, 342, 0, 274, 388, 810, 696, 662, 320, 1084, 514],\n",
+    "      [194, 742, 742, 890, 776, 240, 468, 388, 274, 0, 342, 536, 422, 388, 274, 810, 468],\n",
+    "      [536, 1084, 400, 1232, 1118, 582, 354, 730, 388, 342, 0, 878, 764, 730, 388, 1152, 354],\n",
+    "      [502, 594, 1278, 514, 400, 776, 1004, 468, 810, 536, 878, 0, 114, 308, 650, 274, 844],\n",
+    "      [388, 480, 1164, 628, 514, 662, 890, 354, 696, 422, 764, 114, 0, 194, 536, 388, 730],\n",
+    "      [354, 674, 1130, 822, 708, 628, 856, 320, 662, 388, 730, 308, 194, 0, 342, 422, 536],\n",
+    "      [468, 1016, 788, 1164, 1050, 514, 514, 662, 320, 274, 388, 650, 536, 342, 0, 764, 194],\n",
+    "      [776, 868, 1552, 560, 674, 1050, 1278, 742, 1084, 810, 1152, 274, 388, 422, 764, 0, 798],\n",
+    "      [662, 1210, 754, 1358, 1244, 708, 480, 856, 514, 468, 354, 844, 730, 536, 194, 798, 0],\n",
+    "        # fmt: on\n",
     "    ]\n",
-    "    data['num_vehicles'] = 4\n",
-    "    data['depot'] = 0\n",
+    "    data[\"num_vehicles\"] = 4\n",
+    "    data[\"depot\"] = 0\n",
     "    return data\n",
     "\n",
     "\n",
     "def print_solution(data, manager, routing, solution):\n",
     "    \"\"\"Prints solution on console.\"\"\"\n",
-    "    print(f'Objective: {solution.ObjectiveValue()}')\n",
+    "    print(f\"Objective: {solution.ObjectiveValue()}\")\n",
     "    total_distance = 0\n",
-    "    for vehicle_id in range(data['num_vehicles']):\n",
+    "    for vehicle_id in range(data[\"num_vehicles\"]):\n",
     "        index = routing.Start(vehicle_id)\n",
-    "        plan_output = f'Route for vehicle {vehicle_id}:\\n'\n",
+    "        plan_output = f\"Route for vehicle {vehicle_id}:\\n\"\n",
     "        route_distance = 0\n",
     "        while not routing.IsEnd(index):\n",
-    "            plan_output += f' {manager.IndexToNode(index)} ->'\n",
+    "            plan_output += f\" {manager.IndexToNode(index)} ->\"\n",
     "            previous_index = index\n",
     "            index = solution.Value(routing.NextVar(index))\n",
     "            route_distance += routing.GetArcCostForVehicle(\n",
-    "                previous_index, index, vehicle_id)\n",
-    "        plan_output += f' {manager.IndexToNode(index)}\\n'\n",
-    "        plan_output += f'Distance of the route: {route_distance}m\\n'\n",
+    "                previous_index, index, vehicle_id\n",
+    "            )\n",
+    "        plan_output += f\" {manager.IndexToNode(index)}\\n\"\n",
+    "        plan_output += f\"Distance of the route: {route_distance}m\\n\"\n",
     "        print(plan_output)\n",
     "        total_distance += route_distance\n",
-    "    print(f'Total Distance of all routes: {total_distance}m')\n",
+    "    print(f\"Total Distance of all routes: {total_distance}m\")\n",
     "\n",
     "\n",
     "\n",
@@ -201,20 +153,20 @@
     "    data = create_data_model()\n",
     "\n",
     "    # Create the routing index manager.\n",
-    "    manager = pywrapcp.RoutingIndexManager(len(data['distance_matrix']),\n",
-    "                                           data['num_vehicles'], data['depot'])\n",
+    "    manager = pywrapcp.RoutingIndexManager(\n",
+    "        len(data[\"distance_matrix\"]), data[\"num_vehicles\"], data[\"depot\"]\n",
+    "    )\n",
     "\n",
     "    # Create Routing Model.\n",
     "    routing = pywrapcp.RoutingModel(manager)\n",
     "\n",
-    "\n",
     "    # Create and register a transit callback.\n",
     "    def distance_callback(from_index, to_index):\n",
     "        \"\"\"Returns the distance between the two nodes.\"\"\"\n",
     "        # Convert from routing variable Index to distance matrix NodeIndex.\n",
     "        from_node = manager.IndexToNode(from_index)\n",
     "        to_node = manager.IndexToNode(to_index)\n",
-    "        return data['distance_matrix'][from_node][to_node]\n",
+    "        return data[\"distance_matrix\"][from_node][to_node]\n",
     "\n",
     "    transit_callback_index = routing.RegisterTransitCallback(distance_callback)\n",
     "\n",
@@ -224,7 +176,8 @@
     "    # Setting first solution heuristic.\n",
     "    search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n",
     "    search_parameters.first_solution_strategy = (\n",
-    "        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)\n",
+    "        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC\n",
+    "    )\n",
     "\n",
     "    # Solve the problem.\n",
     "    solution = routing.SolveWithParameters(search_parameters)\n",
@@ -233,7 +186,7 @@
     "    if solution:\n",
     "        print_solution(data, manager, routing, solution)\n",
     "    else:\n",
-    "        print('No solution found !')\n",
+    "        print(\"No solution found !\")\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/constraint_solver/vrp_breaks.ipynb b/examples/notebook/constraint_solver/vrp_breaks.ipynb
index 91d2ae8fb0..258b24561f 100644
--- a/examples/notebook/constraint_solver/vrp_breaks.ipynb
+++ b/examples/notebook/constraint_solver/vrp_breaks.ipynb
@@ -98,9 +98,9 @@
     "def create_data_model():\n",
     "    \"\"\"Stores the data for the problem.\"\"\"\n",
     "    data = {}\n",
-    "    data['num_vehicles'] = 4\n",
-    "    data['depot'] = 0\n",
-    "    data['time_matrix'] = [\n",
+    "    data[\"num_vehicles\"] = 4\n",
+    "    data[\"depot\"] = 0\n",
+    "    data[\"time_matrix\"] = [\n",
     "        [0, 27, 38, 34, 29, 13, 25, 9, 15, 9, 26, 25, 19, 17, 23, 38, 33],\n",
     "        [27, 0, 34, 15, 9, 25, 36, 17, 34, 37, 54, 29, 24, 33, 50, 43, 60],\n",
     "        [38, 34, 0, 49, 43, 25, 13, 40, 23, 37, 20, 63, 58, 56, 39, 77, 37],\n",
@@ -120,43 +120,45 @@
     "        [33, 60, 37, 67, 62, 35, 24, 42, 25, 23, 17, 42, 36, 26, 9, 39, 0],\n",
     "    ]\n",
     "    # 15 min of service time\n",
-    "    data['service_time'] = [15] * len(data['time_matrix'])\n",
-    "    data['service_time'][data['depot']] = 0\n",
-    "    assert len(data['time_matrix']) == len(data['service_time'])\n",
+    "    data[\"service_time\"] = [15] * len(data[\"time_matrix\"])\n",
+    "    data[\"service_time\"][data[\"depot\"]] = 0\n",
+    "    assert len(data[\"time_matrix\"]) == len(data[\"service_time\"])\n",
     "    return data\n",
     "\n",
     "\n",
     "def print_solution(manager, routing, solution):\n",
     "    \"\"\"Prints solution on console.\"\"\"\n",
-    "    print(f'Objective: {solution.ObjectiveValue()}')\n",
+    "    print(f\"Objective: {solution.ObjectiveValue()}\")\n",
     "\n",
-    "    print('Breaks:')\n",
+    "    print(\"Breaks:\")\n",
     "    intervals = solution.IntervalVarContainer()\n",
     "    for i in range(intervals.Size()):\n",
     "        brk = intervals.Element(i)\n",
     "        if brk.PerformedValue():\n",
-    "            print(f'{brk.Var().Name()}: ' +\n",
-    "                  f'Start({brk.StartValue()}) Duration({brk.DurationValue()})')\n",
+    "            print(\n",
+    "                f\"{brk.Var().Name()}: \"\n",
+    "                + f\"Start({brk.StartValue()}) Duration({brk.DurationValue()})\"\n",
+    "            )\n",
     "        else:\n",
-    "            print(f'{brk.Var().Name()}: Unperformed')\n",
+    "            print(f\"{brk.Var().Name()}: Unperformed\")\n",
     "\n",
-    "    time_dimension = routing.GetDimensionOrDie('Time')\n",
+    "    time_dimension = routing.GetDimensionOrDie(\"Time\")\n",
     "    total_time = 0\n",
     "    for vehicle_id in range(manager.GetNumberOfVehicles()):\n",
     "        index = routing.Start(vehicle_id)\n",
-    "        plan_output = f'Route for vehicle {vehicle_id}:\\n'\n",
+    "        plan_output = f\"Route for vehicle {vehicle_id}:\\n\"\n",
     "        while not routing.IsEnd(index):\n",
     "            time_var = time_dimension.CumulVar(index)\n",
-    "            plan_output += f'{manager.IndexToNode(index)} '\n",
-    "            plan_output += f'Time({solution.Value(time_var)}) -> '\n",
+    "            plan_output += f\"{manager.IndexToNode(index)} \"\n",
+    "            plan_output += f\"Time({solution.Value(time_var)}) -> \"\n",
     "            index = solution.Value(routing.NextVar(index))\n",
     "        time_var = time_dimension.CumulVar(index)\n",
-    "        plan_output += f'{manager.IndexToNode(index)} '\n",
-    "        plan_output += f'Time({solution.Value(time_var)})\\n'\n",
-    "        plan_output += f'Time of the route: {solution.Value(time_var)}min\\n'\n",
+    "        plan_output += f\"{manager.IndexToNode(index)} \"\n",
+    "        plan_output += f\"Time({solution.Value(time_var)})\\n\"\n",
+    "        plan_output += f\"Time of the route: {solution.Value(time_var)}min\\n\"\n",
     "        print(plan_output)\n",
     "        total_time += solution.Value(time_var)\n",
-    "    print(f'Total time of all routes: {total_time}min')\n",
+    "    print(f\"Total time of all routes: {total_time}min\")\n",
     "\n",
     "\n",
     "def main():\n",
@@ -165,21 +167,20 @@
     "    data = create_data_model()\n",
     "\n",
     "    # Create the routing index manager.\n",
-    "    manager = pywrapcp.RoutingIndexManager(len(data['time_matrix']),\n",
-    "                                           data['num_vehicles'], data['depot'])\n",
+    "    manager = pywrapcp.RoutingIndexManager(\n",
+    "        len(data[\"time_matrix\"]), data[\"num_vehicles\"], data[\"depot\"]\n",
+    "    )\n",
     "\n",
     "    # Create Routing Model.\n",
     "    routing = pywrapcp.RoutingModel(manager)\n",
     "\n",
-    "\n",
     "    # Create and register a transit callback.\n",
     "    def time_callback(from_index, to_index):\n",
     "        \"\"\"Returns the travel time + service time between the two nodes.\"\"\"\n",
     "        # Convert from routing variable Index to time matrix NodeIndex.\n",
     "        from_node = manager.IndexToNode(from_index)\n",
     "        to_node = manager.IndexToNode(to_index)\n",
-    "        return data['time_matrix'][from_node][to_node] + data['service_time'][\n",
-    "            from_node]\n",
+    "        return data[\"time_matrix\"][from_node][to_node] + data[\"service_time\"][from_node]\n",
     "\n",
     "    transit_callback_index = routing.RegisterTransitCallback(time_callback)\n",
     "\n",
@@ -187,13 +188,14 @@
     "    routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)\n",
     "\n",
     "    # Add Time Windows constraint.\n",
-    "    time = 'Time'\n",
+    "    time = \"Time\"\n",
     "    routing.AddDimension(\n",
     "        transit_callback_index,\n",
     "        10,  # needed optional waiting time to place break\n",
     "        180,  # maximum time per vehicle\n",
     "        True,  # Force start cumul to zero.\n",
-    "        time)\n",
+    "        time,\n",
+    "    )\n",
     "    time_dimension = routing.GetDimensionOrDie(time)\n",
     "    time_dimension.SetGlobalSpanCostCoefficient(10)\n",
     "\n",
@@ -202,7 +204,7 @@
     "    node_visit_transit = [0] * routing.Size()\n",
     "    for index in range(routing.Size()):\n",
     "        node = manager.IndexToNode(index)\n",
-    "        node_visit_transit[index] = data['service_time'][node]\n",
+    "        node_visit_transit[index] = data[\"service_time\"][node]\n",
     "\n",
     "    break_intervals = {}\n",
     "    for v in range(manager.GetNumberOfVehicles()):\n",
@@ -212,19 +214,21 @@
     "                60,  # start max\n",
     "                10,  # duration: 10 min\n",
     "                False,  # optional: no\n",
-    "                f'Break for vehicle {v}')\n",
+    "                f\"Break for vehicle {v}\",\n",
+    "            )\n",
     "        ]\n",
     "        time_dimension.SetBreakIntervalsOfVehicle(\n",
-    "            break_intervals[v],  # breaks\n",
-    "            v,  # vehicle index\n",
-    "            node_visit_transit)\n",
+    "            break_intervals[v], v, node_visit_transit  # breaks  # vehicle index\n",
+    "        )\n",
     "\n",
     "    # Setting first solution heuristic.\n",
     "    search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n",
     "    search_parameters.first_solution_strategy = (\n",
-    "        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)\n",
+    "        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC\n",
+    "    )\n",
     "    search_parameters.local_search_metaheuristic = (\n",
-    "        routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH)\n",
+    "        routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH\n",
+    "    )\n",
     "    # search_parameters.log_search = True\n",
     "    search_parameters.time_limit.FromSeconds(2)\n",
     "\n",
@@ -235,7 +239,7 @@
     "    if solution:\n",
     "        print_solution(manager, routing, solution)\n",
     "    else:\n",
-    "        print('No solution found !')\n",
+    "        print(\"No solution found !\")\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/constraint_solver/vrp_capacity.ipynb b/examples/notebook/constraint_solver/vrp_capacity.ipynb
index 9e947de26d..a74307e04e 100644
--- a/examples/notebook/constraint_solver/vrp_capacity.ipynb
+++ b/examples/notebook/constraint_solver/vrp_capacity.ipynb
@@ -90,109 +90,61 @@
     "def create_data_model():\n",
     "    \"\"\"Stores the data for the problem.\"\"\"\n",
     "    data = {}\n",
-    "    data['distance_matrix'] = [\n",
-    "        [\n",
-    "            0, 548, 776, 696, 582, 274, 502, 194, 308, 194, 536, 502, 388, 354,\n",
-    "            468, 776, 662\n",
-    "        ],\n",
-    "        [\n",
-    "            548, 0, 684, 308, 194, 502, 730, 354, 696, 742, 1084, 594, 480, 674,\n",
-    "            1016, 868, 1210\n",
-    "        ],\n",
-    "        [\n",
-    "            776, 684, 0, 992, 878, 502, 274, 810, 468, 742, 400, 1278, 1164,\n",
-    "            1130, 788, 1552, 754\n",
-    "        ],\n",
-    "        [\n",
-    "            696, 308, 992, 0, 114, 650, 878, 502, 844, 890, 1232, 514, 628, 822,\n",
-    "            1164, 560, 1358\n",
-    "        ],\n",
-    "        [\n",
-    "            582, 194, 878, 114, 0, 536, 764, 388, 730, 776, 1118, 400, 514, 708,\n",
-    "            1050, 674, 1244\n",
-    "        ],\n",
-    "        [\n",
-    "            274, 502, 502, 650, 536, 0, 228, 308, 194, 240, 582, 776, 662, 628,\n",
-    "            514, 1050, 708\n",
-    "        ],\n",
-    "        [\n",
-    "            502, 730, 274, 878, 764, 228, 0, 536, 194, 468, 354, 1004, 890, 856,\n",
-    "            514, 1278, 480\n",
-    "        ],\n",
-    "        [\n",
-    "            194, 354, 810, 502, 388, 308, 536, 0, 342, 388, 730, 468, 354, 320,\n",
-    "            662, 742, 856\n",
-    "        ],\n",
-    "        [\n",
-    "            308, 696, 468, 844, 730, 194, 194, 342, 0, 274, 388, 810, 696, 662,\n",
-    "            320, 1084, 514\n",
-    "        ],\n",
-    "        [\n",
-    "            194, 742, 742, 890, 776, 240, 468, 388, 274, 0, 342, 536, 422, 388,\n",
-    "            274, 810, 468\n",
-    "        ],\n",
-    "        [\n",
-    "            536, 1084, 400, 1232, 1118, 582, 354, 730, 388, 342, 0, 878, 764,\n",
-    "            730, 388, 1152, 354\n",
-    "        ],\n",
-    "        [\n",
-    "            502, 594, 1278, 514, 400, 776, 1004, 468, 810, 536, 878, 0, 114,\n",
-    "            308, 650, 274, 844\n",
-    "        ],\n",
-    "        [\n",
-    "            388, 480, 1164, 628, 514, 662, 890, 354, 696, 422, 764, 114, 0, 194,\n",
-    "            536, 388, 730\n",
-    "        ],\n",
-    "        [\n",
-    "            354, 674, 1130, 822, 708, 628, 856, 320, 662, 388, 730, 308, 194, 0,\n",
-    "            342, 422, 536\n",
-    "        ],\n",
-    "        [\n",
-    "            468, 1016, 788, 1164, 1050, 514, 514, 662, 320, 274, 388, 650, 536,\n",
-    "            342, 0, 764, 194\n",
-    "        ],\n",
-    "        [\n",
-    "            776, 868, 1552, 560, 674, 1050, 1278, 742, 1084, 810, 1152, 274,\n",
-    "            388, 422, 764, 0, 798\n",
-    "        ],\n",
-    "        [\n",
-    "            662, 1210, 754, 1358, 1244, 708, 480, 856, 514, 468, 354, 844, 730,\n",
-    "            536, 194, 798, 0\n",
-    "        ],\n",
+    "    data[\"distance_matrix\"] = [\n",
+    "        # fmt: off\n",
+    "      [0, 548, 776, 696, 582, 274, 502, 194, 308, 194, 536, 502, 388, 354, 468, 776, 662],\n",
+    "      [548, 0, 684, 308, 194, 502, 730, 354, 696, 742, 1084, 594, 480, 674, 1016, 868, 1210],\n",
+    "      [776, 684, 0, 992, 878, 502, 274, 810, 468, 742, 400, 1278, 1164, 1130, 788, 1552, 754],\n",
+    "      [696, 308, 992, 0, 114, 650, 878, 502, 844, 890, 1232, 514, 628, 822, 1164, 560, 1358],\n",
+    "      [582, 194, 878, 114, 0, 536, 764, 388, 730, 776, 1118, 400, 514, 708, 1050, 674, 1244],\n",
+    "      [274, 502, 502, 650, 536, 0, 228, 308, 194, 240, 582, 776, 662, 628, 514, 1050, 708],\n",
+    "      [502, 730, 274, 878, 764, 228, 0, 536, 194, 468, 354, 1004, 890, 856, 514, 1278, 480],\n",
+    "      [194, 354, 810, 502, 388, 308, 536, 0, 342, 388, 730, 468, 354, 320, 662, 742, 856],\n",
+    "      [308, 696, 468, 844, 730, 194, 194, 342, 0, 274, 388, 810, 696, 662, 320, 1084, 514],\n",
+    "      [194, 742, 742, 890, 776, 240, 468, 388, 274, 0, 342, 536, 422, 388, 274, 810, 468],\n",
+    "      [536, 1084, 400, 1232, 1118, 582, 354, 730, 388, 342, 0, 878, 764, 730, 388, 1152, 354],\n",
+    "      [502, 594, 1278, 514, 400, 776, 1004, 468, 810, 536, 878, 0, 114, 308, 650, 274, 844],\n",
+    "      [388, 480, 1164, 628, 514, 662, 890, 354, 696, 422, 764, 114, 0, 194, 536, 388, 730],\n",
+    "      [354, 674, 1130, 822, 708, 628, 856, 320, 662, 388, 730, 308, 194, 0, 342, 422, 536],\n",
+    "      [468, 1016, 788, 1164, 1050, 514, 514, 662, 320, 274, 388, 650, 536, 342, 0, 764, 194],\n",
+    "      [776, 868, 1552, 560, 674, 1050, 1278, 742, 1084, 810, 1152, 274, 388, 422, 764, 0, 798],\n",
+    "      [662, 1210, 754, 1358, 1244, 708, 480, 856, 514, 468, 354, 844, 730, 536, 194, 798, 0],\n",
+    "        # fmt: on\n",
     "    ]\n",
-    "    data['demands'] = [0, 1, 1, 2, 4, 2, 4, 8, 8, 1, 2, 1, 2, 4, 4, 8, 8]\n",
-    "    data['vehicle_capacities'] = [15, 15, 15, 15]\n",
-    "    data['num_vehicles'] = 4\n",
-    "    data['depot'] = 0\n",
+    "    data[\"demands\"] = [0, 1, 1, 2, 4, 2, 4, 8, 8, 1, 2, 1, 2, 4, 4, 8, 8]\n",
+    "    data[\"vehicle_capacities\"] = [15, 15, 15, 15]\n",
+    "    data[\"num_vehicles\"] = 4\n",
+    "    data[\"depot\"] = 0\n",
     "    return data\n",
     "\n",
     "\n",
     "def print_solution(data, manager, routing, solution):\n",
     "    \"\"\"Prints solution on console.\"\"\"\n",
-    "    print(f'Objective: {solution.ObjectiveValue()}')\n",
+    "    print(f\"Objective: {solution.ObjectiveValue()}\")\n",
     "    total_distance = 0\n",
     "    total_load = 0\n",
-    "    for vehicle_id in range(data['num_vehicles']):\n",
+    "    for vehicle_id in range(data[\"num_vehicles\"]):\n",
     "        index = routing.Start(vehicle_id)\n",
-    "        plan_output = f'Route for vehicle {vehicle_id}:\\n'\n",
+    "        plan_output = f\"Route for vehicle {vehicle_id}:\\n\"\n",
     "        route_distance = 0\n",
     "        route_load = 0\n",
     "        while not routing.IsEnd(index):\n",
     "            node_index = manager.IndexToNode(index)\n",
-    "            route_load += data['demands'][node_index]\n",
-    "            plan_output += f' {node_index} Load({route_load}) -> '\n",
+    "            route_load += data[\"demands\"][node_index]\n",
+    "            plan_output += f\" {node_index} Load({route_load}) -> \"\n",
     "            previous_index = index\n",
     "            index = solution.Value(routing.NextVar(index))\n",
     "            route_distance += routing.GetArcCostForVehicle(\n",
-    "                previous_index, index, vehicle_id)\n",
-    "        plan_output += f' {manager.IndexToNode(index)} Load({route_load})\\n'\n",
-    "        plan_output += f'Distance of the route: {route_distance}m\\n'\n",
-    "        plan_output += f'Load of the route: {route_load}\\n'\n",
+    "                previous_index, index, vehicle_id\n",
+    "            )\n",
+    "        plan_output += f\" {manager.IndexToNode(index)} Load({route_load})\\n\"\n",
+    "        plan_output += f\"Distance of the route: {route_distance}m\\n\"\n",
+    "        plan_output += f\"Load of the route: {route_load}\\n\"\n",
     "        print(plan_output)\n",
     "        total_distance += route_distance\n",
     "        total_load += route_load\n",
-    "    print(f'Total distance of all routes: {total_distance}m')\n",
-    "    print(f'Total load of all routes: {total_load}')\n",
+    "    print(f\"Total distance of all routes: {total_distance}m\")\n",
+    "    print(f\"Total load of all routes: {total_load}\")\n",
     "\n",
     "\n",
     "def main():\n",
@@ -201,49 +153,50 @@
     "    data = create_data_model()\n",
     "\n",
     "    # Create the routing index manager.\n",
-    "    manager = pywrapcp.RoutingIndexManager(len(data['distance_matrix']),\n",
-    "                                           data['num_vehicles'], data['depot'])\n",
+    "    manager = pywrapcp.RoutingIndexManager(\n",
+    "        len(data[\"distance_matrix\"]), data[\"num_vehicles\"], data[\"depot\"]\n",
+    "    )\n",
     "\n",
     "    # Create Routing Model.\n",
     "    routing = pywrapcp.RoutingModel(manager)\n",
     "\n",
-    "\n",
     "    # Create and register a transit callback.\n",
     "    def distance_callback(from_index, to_index):\n",
     "        \"\"\"Returns the distance between the two nodes.\"\"\"\n",
     "        # Convert from routing variable Index to distance matrix NodeIndex.\n",
     "        from_node = manager.IndexToNode(from_index)\n",
     "        to_node = manager.IndexToNode(to_index)\n",
-    "        return data['distance_matrix'][from_node][to_node]\n",
+    "        return data[\"distance_matrix\"][from_node][to_node]\n",
     "\n",
     "    transit_callback_index = routing.RegisterTransitCallback(distance_callback)\n",
     "\n",
     "    # Define cost of each arc.\n",
     "    routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)\n",
     "\n",
-    "\n",
     "    # Add Capacity constraint.\n",
     "    def demand_callback(from_index):\n",
     "        \"\"\"Returns the demand of the node.\"\"\"\n",
     "        # Convert from routing variable Index to demands NodeIndex.\n",
     "        from_node = manager.IndexToNode(from_index)\n",
-    "        return data['demands'][from_node]\n",
+    "        return data[\"demands\"][from_node]\n",
     "\n",
-    "    demand_callback_index = routing.RegisterUnaryTransitCallback(\n",
-    "        demand_callback)\n",
+    "    demand_callback_index = routing.RegisterUnaryTransitCallback(demand_callback)\n",
     "    routing.AddDimensionWithVehicleCapacity(\n",
     "        demand_callback_index,\n",
     "        0,  # null capacity slack\n",
-    "        data['vehicle_capacities'],  # vehicle maximum capacities\n",
+    "        data[\"vehicle_capacities\"],  # vehicle maximum capacities\n",
     "        True,  # start cumul to zero\n",
-    "        'Capacity')\n",
+    "        \"Capacity\",\n",
+    "    )\n",
     "\n",
     "    # Setting first solution heuristic.\n",
     "    search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n",
     "    search_parameters.first_solution_strategy = (\n",
-    "        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)\n",
+    "        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC\n",
+    "    )\n",
     "    search_parameters.local_search_metaheuristic = (\n",
-    "        routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH)\n",
+    "        routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH\n",
+    "    )\n",
     "    search_parameters.time_limit.FromSeconds(1)\n",
     "\n",
     "    # Solve the problem.\n",
diff --git a/examples/notebook/constraint_solver/vrp_drop_nodes.ipynb b/examples/notebook/constraint_solver/vrp_drop_nodes.ipynb
index 9def852cc8..f07e42921b 100644
--- a/examples/notebook/constraint_solver/vrp_drop_nodes.ipynb
+++ b/examples/notebook/constraint_solver/vrp_drop_nodes.ipynb
@@ -90,118 +90,70 @@
     "def create_data_model():\n",
     "    \"\"\"Stores the data for the problem.\"\"\"\n",
     "    data = {}\n",
-    "    data['distance_matrix'] = [\n",
-    "        [\n",
-    "            0, 548, 776, 696, 582, 274, 502, 194, 308, 194, 536, 502, 388, 354,\n",
-    "            468, 776, 662\n",
-    "        ],\n",
-    "        [\n",
-    "            548, 0, 684, 308, 194, 502, 730, 354, 696, 742, 1084, 594, 480, 674,\n",
-    "            1016, 868, 1210\n",
-    "        ],\n",
-    "        [\n",
-    "            776, 684, 0, 992, 878, 502, 274, 810, 468, 742, 400, 1278, 1164,\n",
-    "            1130, 788, 1552, 754\n",
-    "        ],\n",
-    "        [\n",
-    "            696, 308, 992, 0, 114, 650, 878, 502, 844, 890, 1232, 514, 628, 822,\n",
-    "            1164, 560, 1358\n",
-    "        ],\n",
-    "        [\n",
-    "            582, 194, 878, 114, 0, 536, 764, 388, 730, 776, 1118, 400, 514, 708,\n",
-    "            1050, 674, 1244\n",
-    "        ],\n",
-    "        [\n",
-    "            274, 502, 502, 650, 536, 0, 228, 308, 194, 240, 582, 776, 662, 628,\n",
-    "            514, 1050, 708\n",
-    "        ],\n",
-    "        [\n",
-    "            502, 730, 274, 878, 764, 228, 0, 536, 194, 468, 354, 1004, 890, 856,\n",
-    "            514, 1278, 480\n",
-    "        ],\n",
-    "        [\n",
-    "            194, 354, 810, 502, 388, 308, 536, 0, 342, 388, 730, 468, 354, 320,\n",
-    "            662, 742, 856\n",
-    "        ],\n",
-    "        [\n",
-    "            308, 696, 468, 844, 730, 194, 194, 342, 0, 274, 388, 810, 696, 662,\n",
-    "            320, 1084, 514\n",
-    "        ],\n",
-    "        [\n",
-    "            194, 742, 742, 890, 776, 240, 468, 388, 274, 0, 342, 536, 422, 388,\n",
-    "            274, 810, 468\n",
-    "        ],\n",
-    "        [\n",
-    "            536, 1084, 400, 1232, 1118, 582, 354, 730, 388, 342, 0, 878, 764,\n",
-    "            730, 388, 1152, 354\n",
-    "        ],\n",
-    "        [\n",
-    "            502, 594, 1278, 514, 400, 776, 1004, 468, 810, 536, 878, 0, 114,\n",
-    "            308, 650, 274, 844\n",
-    "        ],\n",
-    "        [\n",
-    "            388, 480, 1164, 628, 514, 662, 890, 354, 696, 422, 764, 114, 0, 194,\n",
-    "            536, 388, 730\n",
-    "        ],\n",
-    "        [\n",
-    "            354, 674, 1130, 822, 708, 628, 856, 320, 662, 388, 730, 308, 194, 0,\n",
-    "            342, 422, 536\n",
-    "        ],\n",
-    "        [\n",
-    "            468, 1016, 788, 1164, 1050, 514, 514, 662, 320, 274, 388, 650, 536,\n",
-    "            342, 0, 764, 194\n",
-    "        ],\n",
-    "        [\n",
-    "            776, 868, 1552, 560, 674, 1050, 1278, 742, 1084, 810, 1152, 274,\n",
-    "            388, 422, 764, 0, 798\n",
-    "        ],\n",
-    "        [\n",
-    "            662, 1210, 754, 1358, 1244, 708, 480, 856, 514, 468, 354, 844, 730,\n",
-    "            536, 194, 798, 0\n",
-    "        ],\n",
+    "    data[\"distance_matrix\"] = [\n",
+    "        # fmt: off\n",
+    "      [0, 548, 776, 696, 582, 274, 502, 194, 308, 194, 536, 502, 388, 354, 468, 776, 662],\n",
+    "      [548, 0, 684, 308, 194, 502, 730, 354, 696, 742, 1084, 594, 480, 674, 1016, 868, 1210],\n",
+    "      [776, 684, 0, 992, 878, 502, 274, 810, 468, 742, 400, 1278, 1164, 1130, 788, 1552, 754],\n",
+    "      [696, 308, 992, 0, 114, 650, 878, 502, 844, 890, 1232, 514, 628, 822, 1164, 560, 1358],\n",
+    "      [582, 194, 878, 114, 0, 536, 764, 388, 730, 776, 1118, 400, 514, 708, 1050, 674, 1244],\n",
+    "      [274, 502, 502, 650, 536, 0, 228, 308, 194, 240, 582, 776, 662, 628, 514, 1050, 708],\n",
+    "      [502, 730, 274, 878, 764, 228, 0, 536, 194, 468, 354, 1004, 890, 856, 514, 1278, 480],\n",
+    "      [194, 354, 810, 502, 388, 308, 536, 0, 342, 388, 730, 468, 354, 320, 662, 742, 856],\n",
+    "      [308, 696, 468, 844, 730, 194, 194, 342, 0, 274, 388, 810, 696, 662, 320, 1084, 514],\n",
+    "      [194, 742, 742, 890, 776, 240, 468, 388, 274, 0, 342, 536, 422, 388, 274, 810, 468],\n",
+    "      [536, 1084, 400, 1232, 1118, 582, 354, 730, 388, 342, 0, 878, 764, 730, 388, 1152, 354],\n",
+    "      [502, 594, 1278, 514, 400, 776, 1004, 468, 810, 536, 878, 0, 114, 308, 650, 274, 844],\n",
+    "      [388, 480, 1164, 628, 514, 662, 890, 354, 696, 422, 764, 114, 0, 194, 536, 388, 730],\n",
+    "      [354, 674, 1130, 822, 708, 628, 856, 320, 662, 388, 730, 308, 194, 0, 342, 422, 536],\n",
+    "      [468, 1016, 788, 1164, 1050, 514, 514, 662, 320, 274, 388, 650, 536, 342, 0, 764, 194],\n",
+    "      [776, 868, 1552, 560, 674, 1050, 1278, 742, 1084, 810, 1152, 274, 388, 422, 764, 0, 798],\n",
+    "      [662, 1210, 754, 1358, 1244, 708, 480, 856, 514, 468, 354, 844, 730, 536, 194, 798, 0],\n",
+    "        # fmt: on\n",
     "    ]\n",
-    "    data['demands'] = [0, 1, 1, 3, 6, 3, 6, 8, 8, 1, 2, 1, 2, 6, 6, 8, 8]\n",
-    "    data['vehicle_capacities'] = [15, 15, 15, 15]\n",
-    "    data['num_vehicles'] = 4\n",
-    "    data['depot'] = 0\n",
+    "    data[\"demands\"] = [0, 1, 1, 3, 6, 3, 6, 8, 8, 1, 2, 1, 2, 6, 6, 8, 8]\n",
+    "    data[\"vehicle_capacities\"] = [15, 15, 15, 15]\n",
+    "    data[\"num_vehicles\"] = 4\n",
+    "    data[\"depot\"] = 0\n",
     "    return data\n",
     "\n",
     "\n",
     "def print_solution(data, manager, routing, assignment):\n",
     "    \"\"\"Prints assignment on console.\"\"\"\n",
-    "    print(f'Objective: {assignment.ObjectiveValue()}')\n",
+    "    print(f\"Objective: {assignment.ObjectiveValue()}\")\n",
     "    # Display dropped nodes.\n",
-    "    dropped_nodes = 'Dropped nodes:'\n",
+    "    dropped_nodes = \"Dropped nodes:\"\n",
     "    for node in range(routing.Size()):\n",
     "        if routing.IsStart(node) or routing.IsEnd(node):\n",
     "            continue\n",
     "        if assignment.Value(routing.NextVar(node)) == node:\n",
-    "            dropped_nodes += f' {manager.IndexToNode(node)}'\n",
+    "            dropped_nodes += f\" {manager.IndexToNode(node)}\"\n",
     "    print(dropped_nodes)\n",
     "    # Display routes\n",
     "    total_distance = 0\n",
     "    total_load = 0\n",
-    "    for vehicle_id in range(data['num_vehicles']):\n",
+    "    for vehicle_id in range(data[\"num_vehicles\"]):\n",
     "        index = routing.Start(vehicle_id)\n",
-    "        plan_output = f'Route for vehicle {vehicle_id}:\\n'\n",
+    "        plan_output = f\"Route for vehicle {vehicle_id}:\\n\"\n",
     "        route_distance = 0\n",
     "        route_load = 0\n",
     "        while not routing.IsEnd(index):\n",
     "            node_index = manager.IndexToNode(index)\n",
-    "            route_load += data['demands'][node_index]\n",
-    "            plan_output += f' {node_index} Load({route_load}) -> '\n",
+    "            route_load += data[\"demands\"][node_index]\n",
+    "            plan_output += f\" {node_index} Load({route_load}) -> \"\n",
     "            previous_index = index\n",
     "            index = assignment.Value(routing.NextVar(index))\n",
     "            route_distance += routing.GetArcCostForVehicle(\n",
-    "                previous_index, index, vehicle_id)\n",
-    "        plan_output += f' {manager.IndexToNode(index)} Load({route_load})\\n'\n",
-    "        plan_output += f'Distance of the route: {route_distance}m\\n'\n",
-    "        plan_output += f'Load of the route: {route_load}\\n'\n",
+    "                previous_index, index, vehicle_id\n",
+    "            )\n",
+    "        plan_output += f\" {manager.IndexToNode(index)} Load({route_load})\\n\"\n",
+    "        plan_output += f\"Distance of the route: {route_distance}m\\n\"\n",
+    "        plan_output += f\"Load of the route: {route_load}\\n\"\n",
     "        print(plan_output)\n",
     "        total_distance += route_distance\n",
     "        total_load += route_load\n",
-    "    print(f'Total Distance of all routes: {total_distance}m')\n",
-    "    print(f'Total Load of all routes: {total_load}')\n",
+    "    print(f\"Total Distance of all routes: {total_distance}m\")\n",
+    "    print(f\"Total Load of all routes: {total_load}\")\n",
     "\n",
     "\n",
     "def main():\n",
@@ -210,53 +162,54 @@
     "    data = create_data_model()\n",
     "\n",
     "    # Create the routing index manager.\n",
-    "    manager = pywrapcp.RoutingIndexManager(len(data['distance_matrix']),\n",
-    "                                           data['num_vehicles'], data['depot'])\n",
+    "    manager = pywrapcp.RoutingIndexManager(\n",
+    "        len(data[\"distance_matrix\"]), data[\"num_vehicles\"], data[\"depot\"]\n",
+    "    )\n",
     "\n",
     "    # Create Routing Model.\n",
     "    routing = pywrapcp.RoutingModel(manager)\n",
     "\n",
-    "\n",
     "    # Create and register a transit callback.\n",
     "    def distance_callback(from_index, to_index):\n",
     "        \"\"\"Returns the distance between the two nodes.\"\"\"\n",
     "        # Convert from routing variable Index to distance matrix NodeIndex.\n",
     "        from_node = manager.IndexToNode(from_index)\n",
     "        to_node = manager.IndexToNode(to_index)\n",
-    "        return data['distance_matrix'][from_node][to_node]\n",
+    "        return data[\"distance_matrix\"][from_node][to_node]\n",
     "\n",
     "    transit_callback_index = routing.RegisterTransitCallback(distance_callback)\n",
     "\n",
     "    # Define cost of each arc.\n",
     "    routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)\n",
     "\n",
-    "\n",
     "    # Add Capacity constraint.\n",
     "    def demand_callback(from_index):\n",
     "        \"\"\"Returns the demand of the node.\"\"\"\n",
     "        # Convert from routing variable Index to demands NodeIndex.\n",
     "        from_node = manager.IndexToNode(from_index)\n",
-    "        return data['demands'][from_node]\n",
+    "        return data[\"demands\"][from_node]\n",
     "\n",
-    "    demand_callback_index = routing.RegisterUnaryTransitCallback(\n",
-    "        demand_callback)\n",
+    "    demand_callback_index = routing.RegisterUnaryTransitCallback(demand_callback)\n",
     "    routing.AddDimensionWithVehicleCapacity(\n",
     "        demand_callback_index,\n",
     "        0,  # null capacity slack\n",
-    "        data['vehicle_capacities'],  # vehicle maximum capacities\n",
+    "        data[\"vehicle_capacities\"],  # vehicle maximum capacities\n",
     "        True,  # start cumul to zero\n",
-    "        'Capacity')\n",
+    "        \"Capacity\",\n",
+    "    )\n",
     "    # Allow to drop nodes.\n",
     "    penalty = 1000\n",
-    "    for node in range(1, len(data['distance_matrix'])):\n",
+    "    for node in range(1, len(data[\"distance_matrix\"])):\n",
     "        routing.AddDisjunction([manager.NodeToIndex(node)], penalty)\n",
     "\n",
     "    # Setting first solution heuristic.\n",
     "    search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n",
     "    search_parameters.first_solution_strategy = (\n",
-    "        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)\n",
+    "        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC\n",
+    "    )\n",
     "    search_parameters.local_search_metaheuristic = (\n",
-    "        routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH)\n",
+    "        routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH\n",
+    "    )\n",
     "    search_parameters.time_limit.FromSeconds(1)\n",
     "\n",
     "    # Solve the problem.\n",
diff --git a/examples/notebook/constraint_solver/vrp_global_span.ipynb b/examples/notebook/constraint_solver/vrp_global_span.ipynb
index 0463d86974..62a5dad2af 100644
--- a/examples/notebook/constraint_solver/vrp_global_span.ipynb
+++ b/examples/notebook/constraint_solver/vrp_global_span.ipynb
@@ -98,100 +98,52 @@
     "def create_data_model():\n",
     "    \"\"\"Stores the data for the problem.\"\"\"\n",
     "    data = {}\n",
-    "    data['distance_matrix'] = [\n",
-    "        [\n",
-    "            0, 548, 776, 696, 582, 274, 502, 194, 308, 194, 536, 502, 388, 354,\n",
-    "            468, 776, 662\n",
-    "        ],\n",
-    "        [\n",
-    "            548, 0, 684, 308, 194, 502, 730, 354, 696, 742, 1084, 594, 480, 674,\n",
-    "            1016, 868, 1210\n",
-    "        ],\n",
-    "        [\n",
-    "            776, 684, 0, 992, 878, 502, 274, 810, 468, 742, 400, 1278, 1164,\n",
-    "            1130, 788, 1552, 754\n",
-    "        ],\n",
-    "        [\n",
-    "            696, 308, 992, 0, 114, 650, 878, 502, 844, 890, 1232, 514, 628, 822,\n",
-    "            1164, 560, 1358\n",
-    "        ],\n",
-    "        [\n",
-    "            582, 194, 878, 114, 0, 536, 764, 388, 730, 776, 1118, 400, 514, 708,\n",
-    "            1050, 674, 1244\n",
-    "        ],\n",
-    "        [\n",
-    "            274, 502, 502, 650, 536, 0, 228, 308, 194, 240, 582, 776, 662, 628,\n",
-    "            514, 1050, 708\n",
-    "        ],\n",
-    "        [\n",
-    "            502, 730, 274, 878, 764, 228, 0, 536, 194, 468, 354, 1004, 890, 856,\n",
-    "            514, 1278, 480\n",
-    "        ],\n",
-    "        [\n",
-    "            194, 354, 810, 502, 388, 308, 536, 0, 342, 388, 730, 468, 354, 320,\n",
-    "            662, 742, 856\n",
-    "        ],\n",
-    "        [\n",
-    "            308, 696, 468, 844, 730, 194, 194, 342, 0, 274, 388, 810, 696, 662,\n",
-    "            320, 1084, 514\n",
-    "        ],\n",
-    "        [\n",
-    "            194, 742, 742, 890, 776, 240, 468, 388, 274, 0, 342, 536, 422, 388,\n",
-    "            274, 810, 468\n",
-    "        ],\n",
-    "        [\n",
-    "            536, 1084, 400, 1232, 1118, 582, 354, 730, 388, 342, 0, 878, 764,\n",
-    "            730, 388, 1152, 354\n",
-    "        ],\n",
-    "        [\n",
-    "            502, 594, 1278, 514, 400, 776, 1004, 468, 810, 536, 878, 0, 114,\n",
-    "            308, 650, 274, 844\n",
-    "        ],\n",
-    "        [\n",
-    "            388, 480, 1164, 628, 514, 662, 890, 354, 696, 422, 764, 114, 0, 194,\n",
-    "            536, 388, 730\n",
-    "        ],\n",
-    "        [\n",
-    "            354, 674, 1130, 822, 708, 628, 856, 320, 662, 388, 730, 308, 194, 0,\n",
-    "            342, 422, 536\n",
-    "        ],\n",
-    "        [\n",
-    "            468, 1016, 788, 1164, 1050, 514, 514, 662, 320, 274, 388, 650, 536,\n",
-    "            342, 0, 764, 194\n",
-    "        ],\n",
-    "        [\n",
-    "            776, 868, 1552, 560, 674, 1050, 1278, 742, 1084, 810, 1152, 274,\n",
-    "            388, 422, 764, 0, 798\n",
-    "        ],\n",
-    "        [\n",
-    "            662, 1210, 754, 1358, 1244, 708, 480, 856, 514, 468, 354, 844, 730,\n",
-    "            536, 194, 798, 0\n",
-    "        ],\n",
+    "    data[\"distance_matrix\"] = [\n",
+    "        # fmt: off\n",
+    "      [0, 548, 776, 696, 582, 274, 502, 194, 308, 194, 536, 502, 388, 354, 468, 776, 662],\n",
+    "      [548, 0, 684, 308, 194, 502, 730, 354, 696, 742, 1084, 594, 480, 674, 1016, 868, 1210],\n",
+    "      [776, 684, 0, 992, 878, 502, 274, 810, 468, 742, 400, 1278, 1164, 1130, 788, 1552, 754],\n",
+    "      [696, 308, 992, 0, 114, 650, 878, 502, 844, 890, 1232, 514, 628, 822, 1164, 560, 1358],\n",
+    "      [582, 194, 878, 114, 0, 536, 764, 388, 730, 776, 1118, 400, 514, 708, 1050, 674, 1244],\n",
+    "      [274, 502, 502, 650, 536, 0, 228, 308, 194, 240, 582, 776, 662, 628, 514, 1050, 708],\n",
+    "      [502, 730, 274, 878, 764, 228, 0, 536, 194, 468, 354, 1004, 890, 856, 514, 1278, 480],\n",
+    "      [194, 354, 810, 502, 388, 308, 536, 0, 342, 388, 730, 468, 354, 320, 662, 742, 856],\n",
+    "      [308, 696, 468, 844, 730, 194, 194, 342, 0, 274, 388, 810, 696, 662, 320, 1084, 514],\n",
+    "      [194, 742, 742, 890, 776, 240, 468, 388, 274, 0, 342, 536, 422, 388, 274, 810, 468],\n",
+    "      [536, 1084, 400, 1232, 1118, 582, 354, 730, 388, 342, 0, 878, 764, 730, 388, 1152, 354],\n",
+    "      [502, 594, 1278, 514, 400, 776, 1004, 468, 810, 536, 878, 0, 114, 308, 650, 274, 844],\n",
+    "      [388, 480, 1164, 628, 514, 662, 890, 354, 696, 422, 764, 114, 0, 194, 536, 388, 730],\n",
+    "      [354, 674, 1130, 822, 708, 628, 856, 320, 662, 388, 730, 308, 194, 0, 342, 422, 536],\n",
+    "      [468, 1016, 788, 1164, 1050, 514, 514, 662, 320, 274, 388, 650, 536, 342, 0, 764, 194],\n",
+    "      [776, 868, 1552, 560, 674, 1050, 1278, 742, 1084, 810, 1152, 274, 388, 422, 764, 0, 798],\n",
+    "      [662, 1210, 754, 1358, 1244, 708, 480, 856, 514, 468, 354, 844, 730, 536, 194, 798, 0],\n",
+    "        # fmt: on\n",
     "    ]\n",
-    "    data['num_vehicles'] = 4\n",
-    "    data['depot'] = 0\n",
+    "    data[\"num_vehicles\"] = 4\n",
+    "    data[\"depot\"] = 0\n",
     "    return data\n",
     "\n",
     "\n",
     "def print_solution(data, manager, routing, solution):\n",
     "    \"\"\"Prints solution on console.\"\"\"\n",
-    "    print(f'Objective: {solution.ObjectiveValue()}')\n",
+    "    print(f\"Objective: {solution.ObjectiveValue()}\")\n",
     "    max_route_distance = 0\n",
-    "    for vehicle_id in range(data['num_vehicles']):\n",
+    "    for vehicle_id in range(data[\"num_vehicles\"]):\n",
     "        index = routing.Start(vehicle_id)\n",
-    "        plan_output = f'Route for vehicle {vehicle_id}:\\n'\n",
+    "        plan_output = f\"Route for vehicle {vehicle_id}:\\n\"\n",
     "        route_distance = 0\n",
     "        while not routing.IsEnd(index):\n",
-    "            plan_output += f' {manager.IndexToNode(index)} -> '\n",
+    "            plan_output += f\" {manager.IndexToNode(index)} -> \"\n",
     "            previous_index = index\n",
     "            index = solution.Value(routing.NextVar(index))\n",
     "            route_distance += routing.GetArcCostForVehicle(\n",
-    "                previous_index, index, vehicle_id)\n",
-    "        plan_output += f'{manager.IndexToNode(index)}\\n'\n",
-    "        plan_output += f'Distance of the route: {route_distance}m\\n'\n",
+    "                previous_index, index, vehicle_id\n",
+    "            )\n",
+    "        plan_output += f\"{manager.IndexToNode(index)}\\n\"\n",
+    "        plan_output += f\"Distance of the route: {route_distance}m\\n\"\n",
     "        print(plan_output)\n",
     "        max_route_distance = max(route_distance, max_route_distance)\n",
-    "    print(f'Maximum of the route distances: {max_route_distance}m')\n",
+    "    print(f\"Maximum of the route distances: {max_route_distance}m\")\n",
     "\n",
     "\n",
     "\n",
@@ -201,20 +153,20 @@
     "    data = create_data_model()\n",
     "\n",
     "    # Create the routing index manager.\n",
-    "    manager = pywrapcp.RoutingIndexManager(len(data['distance_matrix']),\n",
-    "                                           data['num_vehicles'], data['depot'])\n",
+    "    manager = pywrapcp.RoutingIndexManager(\n",
+    "        len(data[\"distance_matrix\"]), data[\"num_vehicles\"], data[\"depot\"]\n",
+    "    )\n",
     "\n",
     "    # Create Routing Model.\n",
     "    routing = pywrapcp.RoutingModel(manager)\n",
     "\n",
-    "\n",
     "    # Create and register a transit callback.\n",
     "    def distance_callback(from_index, to_index):\n",
     "        \"\"\"Returns the distance between the two nodes.\"\"\"\n",
     "        # Convert from routing variable Index to distance matrix NodeIndex.\n",
     "        from_node = manager.IndexToNode(from_index)\n",
     "        to_node = manager.IndexToNode(to_index)\n",
-    "        return data['distance_matrix'][from_node][to_node]\n",
+    "        return data[\"distance_matrix\"][from_node][to_node]\n",
     "\n",
     "    transit_callback_index = routing.RegisterTransitCallback(distance_callback)\n",
     "\n",
@@ -222,20 +174,22 @@
     "    routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)\n",
     "\n",
     "    # Add Distance constraint.\n",
-    "    dimension_name = 'Distance'\n",
+    "    dimension_name = \"Distance\"\n",
     "    routing.AddDimension(\n",
     "        transit_callback_index,\n",
     "        0,  # no slack\n",
     "        3000,  # vehicle maximum travel distance\n",
     "        True,  # start cumul to zero\n",
-    "        dimension_name)\n",
+    "        dimension_name,\n",
+    "    )\n",
     "    distance_dimension = routing.GetDimensionOrDie(dimension_name)\n",
     "    distance_dimension.SetGlobalSpanCostCoefficient(100)\n",
     "\n",
     "    # Setting first solution heuristic.\n",
     "    search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n",
     "    search_parameters.first_solution_strategy = (\n",
-    "        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)\n",
+    "        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC\n",
+    "    )\n",
     "\n",
     "    # Solve the problem.\n",
     "    solution = routing.SolveWithParameters(search_parameters)\n",
@@ -244,7 +198,7 @@
     "    if solution:\n",
     "        print_solution(data, manager, routing, solution)\n",
     "    else:\n",
-    "        print('No solution found !')\n",
+    "        print(\"No solution found !\")\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/constraint_solver/vrp_initial_routes.ipynb b/examples/notebook/constraint_solver/vrp_initial_routes.ipynb
index 6e53c72952..d7c8bd944e 100644
--- a/examples/notebook/constraint_solver/vrp_initial_routes.ipynb
+++ b/examples/notebook/constraint_solver/vrp_initial_routes.ipynb
@@ -90,106 +90,58 @@
     "def create_data_model():\n",
     "    \"\"\"Stores the data for the problem.\"\"\"\n",
     "    data = {}\n",
-    "    data['distance_matrix'] = [\n",
-    "        [\n",
-    "            0, 548, 776, 696, 582, 274, 502, 194, 308, 194, 536, 502, 388, 354,\n",
-    "            468, 776, 662\n",
-    "        ],\n",
-    "        [\n",
-    "            548, 0, 684, 308, 194, 502, 730, 354, 696, 742, 1084, 594, 480, 674,\n",
-    "            1016, 868, 1210\n",
-    "        ],\n",
-    "        [\n",
-    "            776, 684, 0, 992, 878, 502, 274, 810, 468, 742, 400, 1278, 1164,\n",
-    "            1130, 788, 1552, 754\n",
-    "        ],\n",
-    "        [\n",
-    "            696, 308, 992, 0, 114, 650, 878, 502, 844, 890, 1232, 514, 628, 822,\n",
-    "            1164, 560, 1358\n",
-    "        ],\n",
-    "        [\n",
-    "            582, 194, 878, 114, 0, 536, 764, 388, 730, 776, 1118, 400, 514, 708,\n",
-    "            1050, 674, 1244\n",
-    "        ],\n",
-    "        [\n",
-    "            274, 502, 502, 650, 536, 0, 228, 308, 194, 240, 582, 776, 662, 628,\n",
-    "            514, 1050, 708\n",
-    "        ],\n",
-    "        [\n",
-    "            502, 730, 274, 878, 764, 228, 0, 536, 194, 468, 354, 1004, 890, 856,\n",
-    "            514, 1278, 480\n",
-    "        ],\n",
-    "        [\n",
-    "            194, 354, 810, 502, 388, 308, 536, 0, 342, 388, 730, 468, 354, 320,\n",
-    "            662, 742, 856\n",
-    "        ],\n",
-    "        [\n",
-    "            308, 696, 468, 844, 730, 194, 194, 342, 0, 274, 388, 810, 696, 662,\n",
-    "            320, 1084, 514\n",
-    "        ],\n",
-    "        [\n",
-    "            194, 742, 742, 890, 776, 240, 468, 388, 274, 0, 342, 536, 422, 388,\n",
-    "            274, 810, 468\n",
-    "        ],\n",
-    "        [\n",
-    "            536, 1084, 400, 1232, 1118, 582, 354, 730, 388, 342, 0, 878, 764,\n",
-    "            730, 388, 1152, 354\n",
-    "        ],\n",
-    "        [\n",
-    "            502, 594, 1278, 514, 400, 776, 1004, 468, 810, 536, 878, 0, 114,\n",
-    "            308, 650, 274, 844\n",
-    "        ],\n",
-    "        [\n",
-    "            388, 480, 1164, 628, 514, 662, 890, 354, 696, 422, 764, 114, 0, 194,\n",
-    "            536, 388, 730\n",
-    "        ],\n",
-    "        [\n",
-    "            354, 674, 1130, 822, 708, 628, 856, 320, 662, 388, 730, 308, 194, 0,\n",
-    "            342, 422, 536\n",
-    "        ],\n",
-    "        [\n",
-    "            468, 1016, 788, 1164, 1050, 514, 514, 662, 320, 274, 388, 650, 536,\n",
-    "            342, 0, 764, 194\n",
-    "        ],\n",
-    "        [\n",
-    "            776, 868, 1552, 560, 674, 1050, 1278, 742, 1084, 810, 1152, 274,\n",
-    "            388, 422, 764, 0, 798\n",
-    "        ],\n",
-    "        [\n",
-    "            662, 1210, 754, 1358, 1244, 708, 480, 856, 514, 468, 354, 844, 730,\n",
-    "            536, 194, 798, 0\n",
-    "        ],\n",
+    "    data[\"distance_matrix\"] = [\n",
+    "        # fmt: off\n",
+    "      [0, 548, 776, 696, 582, 274, 502, 194, 308, 194, 536, 502, 388, 354, 468, 776, 662],\n",
+    "      [548, 0, 684, 308, 194, 502, 730, 354, 696, 742, 1084, 594, 480, 674, 1016, 868, 1210],\n",
+    "      [776, 684, 0, 992, 878, 502, 274, 810, 468, 742, 400, 1278, 1164, 1130, 788, 1552, 754],\n",
+    "      [696, 308, 992, 0, 114, 650, 878, 502, 844, 890, 1232, 514, 628, 822, 1164, 560, 1358],\n",
+    "      [582, 194, 878, 114, 0, 536, 764, 388, 730, 776, 1118, 400, 514, 708, 1050, 674, 1244],\n",
+    "      [274, 502, 502, 650, 536, 0, 228, 308, 194, 240, 582, 776, 662, 628, 514, 1050, 708],\n",
+    "      [502, 730, 274, 878, 764, 228, 0, 536, 194, 468, 354, 1004, 890, 856, 514, 1278, 480],\n",
+    "      [194, 354, 810, 502, 388, 308, 536, 0, 342, 388, 730, 468, 354, 320, 662, 742, 856],\n",
+    "      [308, 696, 468, 844, 730, 194, 194, 342, 0, 274, 388, 810, 696, 662, 320, 1084, 514],\n",
+    "      [194, 742, 742, 890, 776, 240, 468, 388, 274, 0, 342, 536, 422, 388, 274, 810, 468],\n",
+    "      [536, 1084, 400, 1232, 1118, 582, 354, 730, 388, 342, 0, 878, 764, 730, 388, 1152, 354],\n",
+    "      [502, 594, 1278, 514, 400, 776, 1004, 468, 810, 536, 878, 0, 114, 308, 650, 274, 844],\n",
+    "      [388, 480, 1164, 628, 514, 662, 890, 354, 696, 422, 764, 114, 0, 194, 536, 388, 730],\n",
+    "      [354, 674, 1130, 822, 708, 628, 856, 320, 662, 388, 730, 308, 194, 0, 342, 422, 536],\n",
+    "      [468, 1016, 788, 1164, 1050, 514, 514, 662, 320, 274, 388, 650, 536, 342, 0, 764, 194],\n",
+    "      [776, 868, 1552, 560, 674, 1050, 1278, 742, 1084, 810, 1152, 274, 388, 422, 764, 0, 798],\n",
+    "      [662, 1210, 754, 1358, 1244, 708, 480, 856, 514, 468, 354, 844, 730, 536, 194, 798, 0],\n",
+    "        # fmt: on\n",
     "    ]\n",
-    "    data['initial_routes'] = [\n",
+    "    data[\"initial_routes\"] = [\n",
     "        [8, 16, 14, 13, 12, 11],\n",
     "        [3, 4, 9, 10],\n",
     "        [15, 1],\n",
     "        [7, 5, 2, 6],\n",
     "    ]\n",
-    "    data['num_vehicles'] = 4\n",
-    "    data['depot'] = 0\n",
+    "    data[\"num_vehicles\"] = 4\n",
+    "    data[\"depot\"] = 0\n",
     "    return data\n",
     "\n",
     "\n",
     "def print_solution(data, manager, routing, solution):\n",
     "    \"\"\"Prints solution on console.\"\"\"\n",
-    "    print(f'Objective: {solution.ObjectiveValue()}')\n",
+    "    print(f\"Objective: {solution.ObjectiveValue()}\")\n",
     "    max_route_distance = 0\n",
-    "    for vehicle_id in range(data['num_vehicles']):\n",
+    "    for vehicle_id in range(data[\"num_vehicles\"]):\n",
     "        index = routing.Start(vehicle_id)\n",
-    "        plan_output = f'Route for vehicle {vehicle_id}:\\n'\n",
+    "        plan_output = f\"Route for vehicle {vehicle_id}:\\n\"\n",
     "        route_distance = 0\n",
     "        while not routing.IsEnd(index):\n",
-    "            plan_output += f' {manager.IndexToNode(index)} -> '\n",
+    "            plan_output += f\" {manager.IndexToNode(index)} -> \"\n",
     "            previous_index = index\n",
     "            index = solution.Value(routing.NextVar(index))\n",
     "            route_distance += routing.GetArcCostForVehicle(\n",
-    "                previous_index, index, vehicle_id)\n",
-    "        plan_output += f'{manager.IndexToNode(index)}\\n'\n",
-    "        plan_output += f'Distance of the route: {route_distance}m\\n'\n",
+    "                previous_index, index, vehicle_id\n",
+    "            )\n",
+    "        plan_output += f\"{manager.IndexToNode(index)}\\n\"\n",
+    "        plan_output += f\"Distance of the route: {route_distance}m\\n\"\n",
     "        print(plan_output)\n",
     "        max_route_distance = max(route_distance, max_route_distance)\n",
-    "    print(f'Maximum of the route distances: {max_route_distance}m')\n",
+    "    print(f\"Maximum of the route distances: {max_route_distance}m\")\n",
     "\n",
     "\n",
     "\n",
@@ -199,20 +151,20 @@
     "    data = create_data_model()\n",
     "\n",
     "    # Create the routing index manager.\n",
-    "    manager = pywrapcp.RoutingIndexManager(len(data['distance_matrix']),\n",
-    "                                           data['num_vehicles'], data['depot'])\n",
+    "    manager = pywrapcp.RoutingIndexManager(\n",
+    "        len(data[\"distance_matrix\"]), data[\"num_vehicles\"], data[\"depot\"]\n",
+    "    )\n",
     "\n",
     "    # Create Routing Model.\n",
     "    routing = pywrapcp.RoutingModel(manager)\n",
     "\n",
-    "\n",
     "    # Create and register a transit callback.\n",
     "    def distance_callback(from_index, to_index):\n",
     "        \"\"\"Returns the distance between the two nodes.\"\"\"\n",
     "        # Convert from routing variable Index to distance matrix NodeIndex.\n",
     "        from_node = manager.IndexToNode(from_index)\n",
     "        to_node = manager.IndexToNode(to_index)\n",
-    "        return data['distance_matrix'][from_node][to_node]\n",
+    "        return data[\"distance_matrix\"][from_node][to_node]\n",
     "\n",
     "    transit_callback_index = routing.RegisterTransitCallback(distance_callback)\n",
     "\n",
@@ -220,13 +172,14 @@
     "    routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)\n",
     "\n",
     "    # Add Distance constraint.\n",
-    "    dimension_name = 'Distance'\n",
+    "    dimension_name = \"Distance\"\n",
     "    routing.AddDimension(\n",
     "        transit_callback_index,\n",
     "        0,  # no slack\n",
     "        3000,  # vehicle maximum travel distance\n",
     "        True,  # start cumul to zero\n",
-    "        dimension_name)\n",
+    "        dimension_name,\n",
+    "    )\n",
     "    distance_dimension = routing.GetDimensionOrDie(dimension_name)\n",
     "    distance_dimension.SetGlobalSpanCostCoefficient(100)\n",
     "\n",
@@ -242,18 +195,18 @@
     "    routing.CloseModelWithParameters(search_parameters)\n",
     "\n",
     "    # Get initial solution from routes after closing the model.\n",
-    "    initial_solution = routing.ReadAssignmentFromRoutes(data['initial_routes'],\n",
-    "                                                        True)\n",
-    "    print('Initial solution:')\n",
+    "    initial_solution = routing.ReadAssignmentFromRoutes(data[\"initial_routes\"], True)\n",
+    "    print(\"Initial solution:\")\n",
     "    print_solution(data, manager, routing, initial_solution)\n",
     "\n",
     "    # Solve the problem.\n",
     "    solution = routing.SolveFromAssignmentWithParameters(\n",
-    "        initial_solution, search_parameters)\n",
+    "        initial_solution, search_parameters\n",
+    "    )\n",
     "\n",
     "    # Print solution on console.\n",
     "    if solution:\n",
-    "        print('Solution after search:')\n",
+    "        print(\"Solution after search:\")\n",
     "        print_solution(data, manager, routing, solution)\n",
     "\n",
     "\n",
diff --git a/examples/notebook/constraint_solver/vrp_node_max.ipynb b/examples/notebook/constraint_solver/vrp_node_max.ipynb
index 6366eca36a..3b8ecc56f9 100644
--- a/examples/notebook/constraint_solver/vrp_node_max.ipynb
+++ b/examples/notebook/constraint_solver/vrp_node_max.ipynb
@@ -94,77 +94,28 @@
     "def create_data_model():\n",
     "    \"\"\"Stores the data for the problem.\"\"\"\n",
     "    data = {}\n",
-    "    data['distance_matrix'] = [\n",
-    "        [\n",
-    "            0, 548, 776, 696, 582, 274, 502, 194, 308, 194, 536, 502, 388, 354,\n",
-    "            468, 776, 662\n",
-    "        ],\n",
-    "        [\n",
-    "            548, 0, 684, 308, 194, 502, 730, 354, 696, 742, 1084, 594, 480, 674,\n",
-    "            1016, 868, 1210\n",
-    "        ],\n",
-    "        [\n",
-    "            776, 684, 0, 992, 878, 502, 274, 810, 468, 742, 400, 1278, 1164,\n",
-    "            1130, 788, 1552, 754\n",
-    "        ],\n",
-    "        [\n",
-    "            696, 308, 992, 0, 114, 650, 878, 502, 844, 890, 1232, 514, 628, 822,\n",
-    "            1164, 560, 1358\n",
-    "        ],\n",
-    "        [\n",
-    "            582, 194, 878, 114, 0, 536, 764, 388, 730, 776, 1118, 400, 514, 708,\n",
-    "            1050, 674, 1244\n",
-    "        ],\n",
-    "        [\n",
-    "            274, 502, 502, 650, 536, 0, 228, 308, 194, 240, 582, 776, 662, 628,\n",
-    "            514, 1050, 708\n",
-    "        ],\n",
-    "        [\n",
-    "            502, 730, 274, 878, 764, 228, 0, 536, 194, 468, 354, 1004, 890, 856,\n",
-    "            514, 1278, 480\n",
-    "        ],\n",
-    "        [\n",
-    "            194, 354, 810, 502, 388, 308, 536, 0, 342, 388, 730, 468, 354, 320,\n",
-    "            662, 742, 856\n",
-    "        ],\n",
-    "        [\n",
-    "            308, 696, 468, 844, 730, 194, 194, 342, 0, 274, 388, 810, 696, 662,\n",
-    "            320, 1084, 514\n",
-    "        ],\n",
-    "        [\n",
-    "            194, 742, 742, 890, 776, 240, 468, 388, 274, 0, 342, 536, 422, 388,\n",
-    "            274, 810, 468\n",
-    "        ],\n",
-    "        [\n",
-    "            536, 1084, 400, 1232, 1118, 582, 354, 730, 388, 342, 0, 878, 764,\n",
-    "            730, 388, 1152, 354\n",
-    "        ],\n",
-    "        [\n",
-    "            502, 594, 1278, 514, 400, 776, 1004, 468, 810, 536, 878, 0, 114,\n",
-    "            308, 650, 274, 844\n",
-    "        ],\n",
-    "        [\n",
-    "            388, 480, 1164, 628, 514, 662, 890, 354, 696, 422, 764, 114, 0, 194,\n",
-    "            536, 388, 730\n",
-    "        ],\n",
-    "        [\n",
-    "            354, 674, 1130, 822, 708, 628, 856, 320, 662, 388, 730, 308, 194, 0,\n",
-    "            342, 422, 536\n",
-    "        ],\n",
-    "        [\n",
-    "            468, 1016, 788, 1164, 1050, 514, 514, 662, 320, 274, 388, 650, 536,\n",
-    "            342, 0, 764, 194\n",
-    "        ],\n",
-    "        [\n",
-    "            776, 868, 1552, 560, 674, 1050, 1278, 742, 1084, 810, 1152, 274,\n",
-    "            388, 422, 764, 0, 798\n",
-    "        ],\n",
-    "        [\n",
-    "            662, 1210, 754, 1358, 1244, 708, 480, 856, 514, 468, 354, 844, 730,\n",
-    "            536, 194, 798, 0\n",
-    "        ],\n",
+    "    data[\"distance_matrix\"] = [\n",
+    "        # fmt: off\n",
+    "      [0, 548, 776, 696, 582, 274, 502, 194, 308, 194, 536, 502, 388, 354, 468, 776, 662],\n",
+    "      [548, 0, 684, 308, 194, 502, 730, 354, 696, 742, 1084, 594, 480, 674, 1016, 868, 1210],\n",
+    "      [776, 684, 0, 992, 878, 502, 274, 810, 468, 742, 400, 1278, 1164, 1130, 788, 1552, 754],\n",
+    "      [696, 308, 992, 0, 114, 650, 878, 502, 844, 890, 1232, 514, 628, 822, 1164, 560, 1358],\n",
+    "      [582, 194, 878, 114, 0, 536, 764, 388, 730, 776, 1118, 400, 514, 708, 1050, 674, 1244],\n",
+    "      [274, 502, 502, 650, 536, 0, 228, 308, 194, 240, 582, 776, 662, 628, 514, 1050, 708],\n",
+    "      [502, 730, 274, 878, 764, 228, 0, 536, 194, 468, 354, 1004, 890, 856, 514, 1278, 480],\n",
+    "      [194, 354, 810, 502, 388, 308, 536, 0, 342, 388, 730, 468, 354, 320, 662, 742, 856],\n",
+    "      [308, 696, 468, 844, 730, 194, 194, 342, 0, 274, 388, 810, 696, 662, 320, 1084, 514],\n",
+    "      [194, 742, 742, 890, 776, 240, 468, 388, 274, 0, 342, 536, 422, 388, 274, 810, 468],\n",
+    "      [536, 1084, 400, 1232, 1118, 582, 354, 730, 388, 342, 0, 878, 764, 730, 388, 1152, 354],\n",
+    "      [502, 594, 1278, 514, 400, 776, 1004, 468, 810, 536, 878, 0, 114, 308, 650, 274, 844],\n",
+    "      [388, 480, 1164, 628, 514, 662, 890, 354, 696, 422, 764, 114, 0, 194, 536, 388, 730],\n",
+    "      [354, 674, 1130, 822, 708, 628, 856, 320, 662, 388, 730, 308, 194, 0, 342, 422, 536],\n",
+    "      [468, 1016, 788, 1164, 1050, 514, 514, 662, 320, 274, 388, 650, 536, 342, 0, 764, 194],\n",
+    "      [776, 868, 1552, 560, 674, 1050, 1278, 742, 1084, 810, 1152, 274, 388, 422, 764, 0, 798],\n",
+    "      [662, 1210, 754, 1358, 1244, 708, 480, 856, 514, 468, 354, 844, 730, 536, 194, 798, 0],\n",
+    "        # fmt: on\n",
     "    ]\n",
-    "    data['value'] = [\n",
+    "    data[\"value\"] = [\n",
     "        0,  # depot\n",
     "        42,  # 1\n",
     "        42,  # 2\n",
@@ -183,23 +134,23 @@
     "        42,  # 15\n",
     "        42,  # 16\n",
     "    ]\n",
-    "    assert len(data['distance_matrix']) == len(data['value'])\n",
-    "    data['num_vehicles'] = 4\n",
-    "    data['depot'] = 0\n",
+    "    assert len(data[\"distance_matrix\"]) == len(data[\"value\"])\n",
+    "    data[\"num_vehicles\"] = 4\n",
+    "    data[\"depot\"] = 0\n",
     "    return data\n",
     "\n",
     "\n",
     "\n",
     "def print_solution(data, manager, routing, solution):\n",
     "    \"\"\"Prints solution on console.\"\"\"\n",
-    "    print(f'Objective: {solution.ObjectiveValue()}')\n",
+    "    print(f\"Objective: {solution.ObjectiveValue()}\")\n",
     "    max_route_distance = 0\n",
-    "    dim_one = routing.GetDimensionOrDie('One')\n",
-    "    dim_two = routing.GetDimensionOrDie('Two')\n",
+    "    dim_one = routing.GetDimensionOrDie(\"One\")\n",
+    "    dim_two = routing.GetDimensionOrDie(\"Two\")\n",
     "\n",
-    "    for vehicle_id in range(data['num_vehicles']):\n",
+    "    for vehicle_id in range(data[\"num_vehicles\"]):\n",
     "        index = routing.Start(vehicle_id)\n",
-    "        plan_output = f'Route for vehicle {vehicle_id}:\\n'\n",
+    "        plan_output = f\"Route for vehicle {vehicle_id}:\\n\"\n",
     "        route_distance = 0\n",
     "        while not routing.IsEnd(index):\n",
     "            one_var = dim_one.CumulVar(index)\n",
@@ -207,23 +158,27 @@
     "            two_var = dim_two.CumulVar(index)\n",
     "            two_slack_var = dim_two.SlackVar(index)\n",
     "            plan_output += (\n",
-    "                f' N:{manager.IndexToNode(index)}'\n",
-    "                f' one:({solution.Value(one_var)}, {solution.Value(one_slack_var)})'\n",
-    "                f' two:({solution.Value(two_var)}, {solution.Value(two_slack_var)})'\n",
-    "                ' -> ')\n",
+    "                f\" N:{manager.IndexToNode(index)}\"\n",
+    "                f\" one:({solution.Value(one_var)}, {solution.Value(one_slack_var)})\"\n",
+    "                f\" two:({solution.Value(two_var)}, {solution.Value(two_slack_var)})\"\n",
+    "                \" -> \"\n",
+    "            )\n",
     "            previous_index = index\n",
     "            index = solution.Value(routing.NextVar(index))\n",
     "            route_distance += routing.GetArcCostForVehicle(\n",
-    "                previous_index, index, vehicle_id)\n",
+    "                previous_index, index, vehicle_id\n",
+    "            )\n",
     "        one_var = dim_one.CumulVar(index)\n",
     "        two_var = dim_two.CumulVar(index)\n",
-    "        plan_output += (f'N:{manager.IndexToNode(index)}'\n",
-    "                        f' one:{solution.Value(one_var)}'\n",
-    "                        f' two:{solution.Value(two_var)}\\n')\n",
-    "        plan_output += f'Distance of the route: {route_distance}m\\n'\n",
+    "        plan_output += (\n",
+    "            f\"N:{manager.IndexToNode(index)}\"\n",
+    "            f\" one:{solution.Value(one_var)}\"\n",
+    "            f\" two:{solution.Value(two_var)}\\n\"\n",
+    "        )\n",
+    "        plan_output += f\"Distance of the route: {route_distance}m\\n\"\n",
     "        print(plan_output)\n",
     "        max_route_distance = max(route_distance, max_route_distance)\n",
-    "    print(f'Maximum of the route distances: {max_route_distance}m')\n",
+    "    print(f\"Maximum of the route distances: {max_route_distance}m\")\n",
     "\n",
     "\n",
     "\n",
@@ -233,8 +188,9 @@
     "    data = create_data_model()\n",
     "\n",
     "    # Create the routing index manager.\n",
-    "    manager = pywrapcp.RoutingIndexManager(len(data['distance_matrix']),\n",
-    "                                           data['num_vehicles'], data['depot'])\n",
+    "    manager = pywrapcp.RoutingIndexManager(\n",
+    "        len(data[\"distance_matrix\"]), data[\"num_vehicles\"], data[\"depot\"]\n",
+    "    )\n",
     "\n",
     "    # Create Routing Model.\n",
     "    routing = pywrapcp.RoutingModel(manager)\n",
@@ -246,7 +202,7 @@
     "        # Convert from routing variable Index to distance matrix NodeIndex.\n",
     "        from_node = manager.IndexToNode(from_index)\n",
     "        to_node = manager.IndexToNode(to_index)\n",
-    "        return data['distance_matrix'][from_node][to_node]\n",
+    "        return data[\"distance_matrix\"][from_node][to_node]\n",
     "\n",
     "    transit_callback_index = routing.RegisterTransitCallback(distance_callback)\n",
     "\n",
@@ -254,13 +210,14 @@
     "    routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)\n",
     "\n",
     "    # Add Distance constraint.\n",
-    "    dimension_name = 'Distance'\n",
+    "    dimension_name = \"Distance\"\n",
     "    routing.AddDimension(\n",
     "        transit_callback_index,\n",
     "        0,  # no slack\n",
     "        3_000,  # vehicle maximum travel distance\n",
     "        True,  # start cumul to zero\n",
-    "        dimension_name)\n",
+    "        dimension_name,\n",
+    "    )\n",
     "    distance_dimension = routing.GetDimensionOrDie(dimension_name)\n",
     "    distance_dimension.SetGlobalSpanCostCoefficient(10)\n",
     "\n",
@@ -272,8 +229,9 @@
     "        42 * 16,  # capacity: be able to store PEAK*ROUTE_LENGTH in worst case\n",
     "        42,  # slack_max: to be able to store peak in slack\n",
     "        True,  #  Fix StartCumulToZero not really matter here\n",
-    "        'One')\n",
-    "    dim_one = routing.GetDimensionOrDie('One')\n",
+    "        \"One\",\n",
+    "    )\n",
+    "    dim_one = routing.GetDimensionOrDie(\"One\")\n",
     "\n",
     "    # Dimension Two will be used to store the max node value in the route end node\n",
     "    # CumulVar so we can use it as an objective cost.\n",
@@ -282,16 +240,17 @@
     "        42 * 16,  # capacity: be able to have PEAK value in CumulVar(End)\n",
     "        42,  # slack_max: to be able to store peak in slack\n",
     "        True,  #  Fix StartCumulToZero YES here\n",
-    "        'Two')\n",
-    "    dim_two = routing.GetDimensionOrDie('Two')\n",
+    "        \"Two\",\n",
+    "    )\n",
+    "    dim_two = routing.GetDimensionOrDie(\"Two\")\n",
     "\n",
     "    # force depot Slack to be value since we don't have any predecessor...\n",
     "    for v in range(manager.GetNumberOfVehicles()):\n",
     "        start = routing.Start(v)\n",
-    "        dim_one.SlackVar(start).SetValue(data['value'][0])\n",
+    "        dim_one.SlackVar(start).SetValue(data[\"value\"][0])\n",
     "        routing.AddToAssignment(dim_one.SlackVar(start))\n",
     "\n",
-    "        dim_two.SlackVar(start).SetValue(data['value'][0])\n",
+    "        dim_two.SlackVar(start).SetValue(data[\"value\"][0])\n",
     "        routing.AddToAssignment(dim_two.SlackVar(start))\n",
     "\n",
     "    # Step by step relation\n",
@@ -305,14 +264,12 @@
     "        for v in range(manager.GetNumberOfVehicles()):\n",
     "            previous_index = routing.Start(v)\n",
     "            cond = routing.NextVar(previous_index) == index\n",
-    "            value = solver.Max(dim_one.SlackVar(previous_index),\n",
-    "                               data['value'][node])\n",
+    "            value = solver.Max(dim_one.SlackVar(previous_index), data[\"value\"][node])\n",
     "            test.append((cond * value).Var())\n",
     "        for previous in range(1, 17):\n",
     "            previous_index = manager.NodeToIndex(previous)\n",
     "            cond = routing.NextVar(previous_index) == index\n",
-    "            value = solver.Max(dim_one.SlackVar(previous_index),\n",
-    "                               data['value'][node])\n",
+    "            value = solver.Max(dim_one.SlackVar(previous_index), data[\"value\"][node])\n",
     "            test.append((cond * value).Var())\n",
     "        solver.Add(solver.Sum(test) == dim_one.SlackVar(index))\n",
     "\n",
@@ -335,9 +292,11 @@
     "    # Setting first solution heuristic.\n",
     "    search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n",
     "    search_parameters.first_solution_strategy = (\n",
-    "        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)\n",
+    "        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC\n",
+    "    )\n",
     "    search_parameters.local_search_metaheuristic = (\n",
-    "        routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH)\n",
+    "        routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH\n",
+    "    )\n",
     "    # search_parameters.log_search = True\n",
     "    search_parameters.time_limit.FromSeconds(5)\n",
     "\n",
@@ -348,7 +307,7 @@
     "    if solution:\n",
     "        print_solution(data, manager, routing, solution)\n",
     "    else:\n",
-    "        print('No solution found !')\n",
+    "        print(\"No solution found !\")\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/constraint_solver/vrp_pickup_delivery.ipynb b/examples/notebook/constraint_solver/vrp_pickup_delivery.ipynb
index ff07859ca3..7080724dc3 100644
--- a/examples/notebook/constraint_solver/vrp_pickup_delivery.ipynb
+++ b/examples/notebook/constraint_solver/vrp_pickup_delivery.ipynb
@@ -90,77 +90,28 @@
     "def create_data_model():\n",
     "    \"\"\"Stores the data for the problem.\"\"\"\n",
     "    data = {}\n",
-    "    data['distance_matrix'] = [\n",
-    "        [\n",
-    "            0, 548, 776, 696, 582, 274, 502, 194, 308, 194, 536, 502, 388, 354,\n",
-    "            468, 776, 662\n",
-    "        ],\n",
-    "        [\n",
-    "            548, 0, 684, 308, 194, 502, 730, 354, 696, 742, 1084, 594, 480, 674,\n",
-    "            1016, 868, 1210\n",
-    "        ],\n",
-    "        [\n",
-    "            776, 684, 0, 992, 878, 502, 274, 810, 468, 742, 400, 1278, 1164,\n",
-    "            1130, 788, 1552, 754\n",
-    "        ],\n",
-    "        [\n",
-    "            696, 308, 992, 0, 114, 650, 878, 502, 844, 890, 1232, 514, 628, 822,\n",
-    "            1164, 560, 1358\n",
-    "        ],\n",
-    "        [\n",
-    "            582, 194, 878, 114, 0, 536, 764, 388, 730, 776, 1118, 400, 514, 708,\n",
-    "            1050, 674, 1244\n",
-    "        ],\n",
-    "        [\n",
-    "            274, 502, 502, 650, 536, 0, 228, 308, 194, 240, 582, 776, 662, 628,\n",
-    "            514, 1050, 708\n",
-    "        ],\n",
-    "        [\n",
-    "            502, 730, 274, 878, 764, 228, 0, 536, 194, 468, 354, 1004, 890, 856,\n",
-    "            514, 1278, 480\n",
-    "        ],\n",
-    "        [\n",
-    "            194, 354, 810, 502, 388, 308, 536, 0, 342, 388, 730, 468, 354, 320,\n",
-    "            662, 742, 856\n",
-    "        ],\n",
-    "        [\n",
-    "            308, 696, 468, 844, 730, 194, 194, 342, 0, 274, 388, 810, 696, 662,\n",
-    "            320, 1084, 514\n",
-    "        ],\n",
-    "        [\n",
-    "            194, 742, 742, 890, 776, 240, 468, 388, 274, 0, 342, 536, 422, 388,\n",
-    "            274, 810, 468\n",
-    "        ],\n",
-    "        [\n",
-    "            536, 1084, 400, 1232, 1118, 582, 354, 730, 388, 342, 0, 878, 764,\n",
-    "            730, 388, 1152, 354\n",
-    "        ],\n",
-    "        [\n",
-    "            502, 594, 1278, 514, 400, 776, 1004, 468, 810, 536, 878, 0, 114,\n",
-    "            308, 650, 274, 844\n",
-    "        ],\n",
-    "        [\n",
-    "            388, 480, 1164, 628, 514, 662, 890, 354, 696, 422, 764, 114, 0, 194,\n",
-    "            536, 388, 730\n",
-    "        ],\n",
-    "        [\n",
-    "            354, 674, 1130, 822, 708, 628, 856, 320, 662, 388, 730, 308, 194, 0,\n",
-    "            342, 422, 536\n",
-    "        ],\n",
-    "        [\n",
-    "            468, 1016, 788, 1164, 1050, 514, 514, 662, 320, 274, 388, 650, 536,\n",
-    "            342, 0, 764, 194\n",
-    "        ],\n",
-    "        [\n",
-    "            776, 868, 1552, 560, 674, 1050, 1278, 742, 1084, 810, 1152, 274,\n",
-    "            388, 422, 764, 0, 798\n",
-    "        ],\n",
-    "        [\n",
-    "            662, 1210, 754, 1358, 1244, 708, 480, 856, 514, 468, 354, 844, 730,\n",
-    "            536, 194, 798, 0\n",
-    "        ],\n",
+    "    data[\"distance_matrix\"] = [\n",
+    "        # fmt: off\n",
+    "      [0, 548, 776, 696, 582, 274, 502, 194, 308, 194, 536, 502, 388, 354, 468, 776, 662],\n",
+    "      [548, 0, 684, 308, 194, 502, 730, 354, 696, 742, 1084, 594, 480, 674, 1016, 868, 1210],\n",
+    "      [776, 684, 0, 992, 878, 502, 274, 810, 468, 742, 400, 1278, 1164, 1130, 788, 1552, 754],\n",
+    "      [696, 308, 992, 0, 114, 650, 878, 502, 844, 890, 1232, 514, 628, 822, 1164, 560, 1358],\n",
+    "      [582, 194, 878, 114, 0, 536, 764, 388, 730, 776, 1118, 400, 514, 708, 1050, 674, 1244],\n",
+    "      [274, 502, 502, 650, 536, 0, 228, 308, 194, 240, 582, 776, 662, 628, 514, 1050, 708],\n",
+    "      [502, 730, 274, 878, 764, 228, 0, 536, 194, 468, 354, 1004, 890, 856, 514, 1278, 480],\n",
+    "      [194, 354, 810, 502, 388, 308, 536, 0, 342, 388, 730, 468, 354, 320, 662, 742, 856],\n",
+    "      [308, 696, 468, 844, 730, 194, 194, 342, 0, 274, 388, 810, 696, 662, 320, 1084, 514],\n",
+    "      [194, 742, 742, 890, 776, 240, 468, 388, 274, 0, 342, 536, 422, 388, 274, 810, 468],\n",
+    "      [536, 1084, 400, 1232, 1118, 582, 354, 730, 388, 342, 0, 878, 764, 730, 388, 1152, 354],\n",
+    "      [502, 594, 1278, 514, 400, 776, 1004, 468, 810, 536, 878, 0, 114, 308, 650, 274, 844],\n",
+    "      [388, 480, 1164, 628, 514, 662, 890, 354, 696, 422, 764, 114, 0, 194, 536, 388, 730],\n",
+    "      [354, 674, 1130, 822, 708, 628, 856, 320, 662, 388, 730, 308, 194, 0, 342, 422, 536],\n",
+    "      [468, 1016, 788, 1164, 1050, 514, 514, 662, 320, 274, 388, 650, 536, 342, 0, 764, 194],\n",
+    "      [776, 868, 1552, 560, 674, 1050, 1278, 742, 1084, 810, 1152, 274, 388, 422, 764, 0, 798],\n",
+    "      [662, 1210, 754, 1358, 1244, 708, 480, 856, 514, 468, 354, 844, 730, 536, 194, 798, 0],\n",
+    "        # fmt: on\n",
     "    ]\n",
-    "    data['pickups_deliveries'] = [\n",
+    "    data[\"pickups_deliveries\"] = [\n",
     "        [1, 6],\n",
     "        [2, 10],\n",
     "        [4, 3],\n",
@@ -170,30 +121,31 @@
     "        [13, 12],\n",
     "        [16, 14],\n",
     "    ]\n",
-    "    data['num_vehicles'] = 4\n",
-    "    data['depot'] = 0\n",
+    "    data[\"num_vehicles\"] = 4\n",
+    "    data[\"depot\"] = 0\n",
     "    return data\n",
     "\n",
     "\n",
     "def print_solution(data, manager, routing, solution):\n",
     "    \"\"\"Prints solution on console.\"\"\"\n",
-    "    print(f'Objective: {solution.ObjectiveValue()}')\n",
+    "    print(f\"Objective: {solution.ObjectiveValue()}\")\n",
     "    total_distance = 0\n",
-    "    for vehicle_id in range(data['num_vehicles']):\n",
+    "    for vehicle_id in range(data[\"num_vehicles\"]):\n",
     "        index = routing.Start(vehicle_id)\n",
-    "        plan_output = f'Route for vehicle {vehicle_id}:\\n'\n",
+    "        plan_output = f\"Route for vehicle {vehicle_id}:\\n\"\n",
     "        route_distance = 0\n",
     "        while not routing.IsEnd(index):\n",
-    "            plan_output += f' {manager.IndexToNode(index)} -> '\n",
+    "            plan_output += f\" {manager.IndexToNode(index)} -> \"\n",
     "            previous_index = index\n",
     "            index = solution.Value(routing.NextVar(index))\n",
     "            route_distance += routing.GetArcCostForVehicle(\n",
-    "                previous_index, index, vehicle_id)\n",
-    "        plan_output += f'{manager.IndexToNode(index)}\\n'\n",
-    "        plan_output += f'Distance of the route: {route_distance}m\\n'\n",
+    "                previous_index, index, vehicle_id\n",
+    "            )\n",
+    "        plan_output += f\"{manager.IndexToNode(index)}\\n\"\n",
+    "        plan_output += f\"Distance of the route: {route_distance}m\\n\"\n",
     "        print(plan_output)\n",
     "        total_distance += route_distance\n",
-    "    print(f'Total Distance of all routes: {total_distance}m')\n",
+    "    print(f\"Total Distance of all routes: {total_distance}m\")\n",
     "\n",
     "\n",
     "def main():\n",
@@ -202,8 +154,9 @@
     "    data = create_data_model()\n",
     "\n",
     "    # Create the routing index manager.\n",
-    "    manager = pywrapcp.RoutingIndexManager(len(data['distance_matrix']),\n",
-    "                                           data['num_vehicles'], data['depot'])\n",
+    "    manager = pywrapcp.RoutingIndexManager(\n",
+    "        len(data[\"distance_matrix\"]), data[\"num_vehicles\"], data[\"depot\"]\n",
+    "    )\n",
     "\n",
     "    # Create Routing Model.\n",
     "    routing = pywrapcp.RoutingModel(manager)\n",
@@ -215,38 +168,41 @@
     "        # Convert from routing variable Index to distance matrix NodeIndex.\n",
     "        from_node = manager.IndexToNode(from_index)\n",
     "        to_node = manager.IndexToNode(to_index)\n",
-    "        return data['distance_matrix'][from_node][to_node]\n",
+    "        return data[\"distance_matrix\"][from_node][to_node]\n",
     "\n",
     "    transit_callback_index = routing.RegisterTransitCallback(distance_callback)\n",
     "    routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)\n",
     "\n",
     "    # Add Distance constraint.\n",
-    "    dimension_name = 'Distance'\n",
+    "    dimension_name = \"Distance\"\n",
     "    routing.AddDimension(\n",
     "        transit_callback_index,\n",
     "        0,  # no slack\n",
     "        3000,  # vehicle maximum travel distance\n",
     "        True,  # start cumul to zero\n",
-    "        dimension_name)\n",
+    "        dimension_name,\n",
+    "    )\n",
     "    distance_dimension = routing.GetDimensionOrDie(dimension_name)\n",
     "    distance_dimension.SetGlobalSpanCostCoefficient(100)\n",
     "\n",
     "    # Define Transportation Requests.\n",
-    "    for request in data['pickups_deliveries']:\n",
+    "    for request in data[\"pickups_deliveries\"]:\n",
     "        pickup_index = manager.NodeToIndex(request[0])\n",
     "        delivery_index = manager.NodeToIndex(request[1])\n",
     "        routing.AddPickupAndDelivery(pickup_index, delivery_index)\n",
     "        routing.solver().Add(\n",
-    "            routing.VehicleVar(pickup_index) == routing.VehicleVar(\n",
-    "                delivery_index))\n",
+    "            routing.VehicleVar(pickup_index) == routing.VehicleVar(delivery_index)\n",
+    "        )\n",
     "        routing.solver().Add(\n",
-    "            distance_dimension.CumulVar(pickup_index) <=\n",
-    "            distance_dimension.CumulVar(delivery_index))\n",
+    "            distance_dimension.CumulVar(pickup_index)\n",
+    "            <= distance_dimension.CumulVar(delivery_index)\n",
+    "        )\n",
     "\n",
     "    # Setting first solution heuristic.\n",
     "    search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n",
     "    search_parameters.first_solution_strategy = (\n",
-    "        routing_enums_pb2.FirstSolutionStrategy.PARALLEL_CHEAPEST_INSERTION)\n",
+    "        routing_enums_pb2.FirstSolutionStrategy.PARALLEL_CHEAPEST_INSERTION\n",
+    "    )\n",
     "\n",
     "    # Solve the problem.\n",
     "    solution = routing.SolveWithParameters(search_parameters)\n",
diff --git a/examples/notebook/constraint_solver/vrp_pickup_delivery_fifo.ipynb b/examples/notebook/constraint_solver/vrp_pickup_delivery_fifo.ipynb
index 3d8e58af04..7d9adc7b4f 100644
--- a/examples/notebook/constraint_solver/vrp_pickup_delivery_fifo.ipynb
+++ b/examples/notebook/constraint_solver/vrp_pickup_delivery_fifo.ipynb
@@ -90,77 +90,28 @@
     "def create_data_model():\n",
     "    \"\"\"Stores the data for the problem.\"\"\"\n",
     "    data = {}\n",
-    "    data['distance_matrix'] = [\n",
-    "        [\n",
-    "            0, 548, 776, 696, 582, 274, 502, 194, 308, 194, 536, 502, 388, 354,\n",
-    "            468, 776, 662\n",
-    "        ],\n",
-    "        [\n",
-    "            548, 0, 684, 308, 194, 502, 730, 354, 696, 742, 1084, 594, 480, 674,\n",
-    "            1016, 868, 1210\n",
-    "        ],\n",
-    "        [\n",
-    "            776, 684, 0, 992, 878, 502, 274, 810, 468, 742, 400, 1278, 1164,\n",
-    "            1130, 788, 1552, 754\n",
-    "        ],\n",
-    "        [\n",
-    "            696, 308, 992, 0, 114, 650, 878, 502, 844, 890, 1232, 514, 628, 822,\n",
-    "            1164, 560, 1358\n",
-    "        ],\n",
-    "        [\n",
-    "            582, 194, 878, 114, 0, 536, 764, 388, 730, 776, 1118, 400, 514, 708,\n",
-    "            1050, 674, 1244\n",
-    "        ],\n",
-    "        [\n",
-    "            274, 502, 502, 650, 536, 0, 228, 308, 194, 240, 582, 776, 662, 628,\n",
-    "            514, 1050, 708\n",
-    "        ],\n",
-    "        [\n",
-    "            502, 730, 274, 878, 764, 228, 0, 536, 194, 468, 354, 1004, 890, 856,\n",
-    "            514, 1278, 480\n",
-    "        ],\n",
-    "        [\n",
-    "            194, 354, 810, 502, 388, 308, 536, 0, 342, 388, 730, 468, 354, 320,\n",
-    "            662, 742, 856\n",
-    "        ],\n",
-    "        [\n",
-    "            308, 696, 468, 844, 730, 194, 194, 342, 0, 274, 388, 810, 696, 662,\n",
-    "            320, 1084, 514\n",
-    "        ],\n",
-    "        [\n",
-    "            194, 742, 742, 890, 776, 240, 468, 388, 274, 0, 342, 536, 422, 388,\n",
-    "            274, 810, 468\n",
-    "        ],\n",
-    "        [\n",
-    "            536, 1084, 400, 1232, 1118, 582, 354, 730, 388, 342, 0, 878, 764,\n",
-    "            730, 388, 1152, 354\n",
-    "        ],\n",
-    "        [\n",
-    "            502, 594, 1278, 514, 400, 776, 1004, 468, 810, 536, 878, 0, 114,\n",
-    "            308, 650, 274, 844\n",
-    "        ],\n",
-    "        [\n",
-    "            388, 480, 1164, 628, 514, 662, 890, 354, 696, 422, 764, 114, 0, 194,\n",
-    "            536, 388, 730\n",
-    "        ],\n",
-    "        [\n",
-    "            354, 674, 1130, 822, 708, 628, 856, 320, 662, 388, 730, 308, 194, 0,\n",
-    "            342, 422, 536\n",
-    "        ],\n",
-    "        [\n",
-    "            468, 1016, 788, 1164, 1050, 514, 514, 662, 320, 274, 388, 650, 536,\n",
-    "            342, 0, 764, 194\n",
-    "        ],\n",
-    "        [\n",
-    "            776, 868, 1552, 560, 674, 1050, 1278, 742, 1084, 810, 1152, 274,\n",
-    "            388, 422, 764, 0, 798\n",
-    "        ],\n",
-    "        [\n",
-    "            662, 1210, 754, 1358, 1244, 708, 480, 856, 514, 468, 354, 844, 730,\n",
-    "            536, 194, 798, 0\n",
-    "        ],\n",
+    "    data[\"distance_matrix\"] = [\n",
+    "        # fmt: off\n",
+    "      [0, 548, 776, 696, 582, 274, 502, 194, 308, 194, 536, 502, 388, 354, 468, 776, 662],\n",
+    "      [548, 0, 684, 308, 194, 502, 730, 354, 696, 742, 1084, 594, 480, 674, 1016, 868, 1210],\n",
+    "      [776, 684, 0, 992, 878, 502, 274, 810, 468, 742, 400, 1278, 1164, 1130, 788, 1552, 754],\n",
+    "      [696, 308, 992, 0, 114, 650, 878, 502, 844, 890, 1232, 514, 628, 822, 1164, 560, 1358],\n",
+    "      [582, 194, 878, 114, 0, 536, 764, 388, 730, 776, 1118, 400, 514, 708, 1050, 674, 1244],\n",
+    "      [274, 502, 502, 650, 536, 0, 228, 308, 194, 240, 582, 776, 662, 628, 514, 1050, 708],\n",
+    "      [502, 730, 274, 878, 764, 228, 0, 536, 194, 468, 354, 1004, 890, 856, 514, 1278, 480],\n",
+    "      [194, 354, 810, 502, 388, 308, 536, 0, 342, 388, 730, 468, 354, 320, 662, 742, 856],\n",
+    "      [308, 696, 468, 844, 730, 194, 194, 342, 0, 274, 388, 810, 696, 662, 320, 1084, 514],\n",
+    "      [194, 742, 742, 890, 776, 240, 468, 388, 274, 0, 342, 536, 422, 388, 274, 810, 468],\n",
+    "      [536, 1084, 400, 1232, 1118, 582, 354, 730, 388, 342, 0, 878, 764, 730, 388, 1152, 354],\n",
+    "      [502, 594, 1278, 514, 400, 776, 1004, 468, 810, 536, 878, 0, 114, 308, 650, 274, 844],\n",
+    "      [388, 480, 1164, 628, 514, 662, 890, 354, 696, 422, 764, 114, 0, 194, 536, 388, 730],\n",
+    "      [354, 674, 1130, 822, 708, 628, 856, 320, 662, 388, 730, 308, 194, 0, 342, 422, 536],\n",
+    "      [468, 1016, 788, 1164, 1050, 514, 514, 662, 320, 274, 388, 650, 536, 342, 0, 764, 194],\n",
+    "      [776, 868, 1552, 560, 674, 1050, 1278, 742, 1084, 810, 1152, 274, 388, 422, 764, 0, 798],\n",
+    "      [662, 1210, 754, 1358, 1244, 708, 480, 856, 514, 468, 354, 844, 730, 536, 194, 798, 0],\n",
+    "        # fmt: on\n",
     "    ]\n",
-    "    data['pickups_deliveries'] = [\n",
+    "    data[\"pickups_deliveries\"] = [\n",
     "        [1, 6],\n",
     "        [2, 10],\n",
     "        [4, 3],\n",
@@ -170,30 +121,31 @@
     "        [13, 12],\n",
     "        [16, 14],\n",
     "    ]\n",
-    "    data['num_vehicles'] = 4\n",
-    "    data['depot'] = 0\n",
+    "    data[\"num_vehicles\"] = 4\n",
+    "    data[\"depot\"] = 0\n",
     "    return data\n",
     "\n",
     "\n",
     "def print_solution(data, manager, routing, assignment):\n",
     "    \"\"\"Prints assignment on console.\"\"\"\n",
-    "    print(f'Objective: {assignment.ObjectiveValue()}')\n",
+    "    print(f\"Objective: {assignment.ObjectiveValue()}\")\n",
     "    total_distance = 0\n",
-    "    for vehicle_id in range(data['num_vehicles']):\n",
+    "    for vehicle_id in range(data[\"num_vehicles\"]):\n",
     "        index = routing.Start(vehicle_id)\n",
-    "        plan_output = f'Route for vehicle {vehicle_id}:\\n'\n",
+    "        plan_output = f\"Route for vehicle {vehicle_id}:\\n\"\n",
     "        route_distance = 0\n",
     "        while not routing.IsEnd(index):\n",
-    "            plan_output += f' {manager.IndexToNode(index)} -> '\n",
+    "            plan_output += f\" {manager.IndexToNode(index)} -> \"\n",
     "            previous_index = index\n",
     "            index = assignment.Value(routing.NextVar(index))\n",
     "            route_distance += routing.GetArcCostForVehicle(\n",
-    "                previous_index, index, vehicle_id)\n",
-    "        plan_output += f'{manager.IndexToNode(index)}\\n'\n",
-    "        plan_output += f'Distance of the route: {route_distance}m\\n'\n",
+    "                previous_index, index, vehicle_id\n",
+    "            )\n",
+    "        plan_output += f\"{manager.IndexToNode(index)}\\n\"\n",
+    "        plan_output += f\"Distance of the route: {route_distance}m\\n\"\n",
     "        print(plan_output)\n",
     "        total_distance += route_distance\n",
-    "    print(f'Total Distance of all routes: {total_distance}m')\n",
+    "    print(f\"Total Distance of all routes: {total_distance}m\")\n",
     "\n",
     "\n",
     "def main():\n",
@@ -202,8 +154,9 @@
     "    data = create_data_model()\n",
     "\n",
     "    # Create the routing index manager.\n",
-    "    manager = pywrapcp.RoutingIndexManager(len(data['distance_matrix']),\n",
-    "                                           data['num_vehicles'], data['depot'])\n",
+    "    manager = pywrapcp.RoutingIndexManager(\n",
+    "        len(data[\"distance_matrix\"]), data[\"num_vehicles\"], data[\"depot\"]\n",
+    "    )\n",
     "\n",
     "    # Create Routing Model.\n",
     "    routing = pywrapcp.RoutingModel(manager)\n",
@@ -215,40 +168,44 @@
     "        # Convert from routing variable Index to distance matrix NodeIndex.\n",
     "        from_node = manager.IndexToNode(from_index)\n",
     "        to_node = manager.IndexToNode(to_index)\n",
-    "        return data['distance_matrix'][from_node][to_node]\n",
+    "        return data[\"distance_matrix\"][from_node][to_node]\n",
     "\n",
     "    transit_callback_index = routing.RegisterTransitCallback(distance_callback)\n",
     "    routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)\n",
     "\n",
     "    # Add Distance constraint.\n",
-    "    dimension_name = 'Distance'\n",
+    "    dimension_name = \"Distance\"\n",
     "    routing.AddDimension(\n",
     "        transit_callback_index,\n",
     "        0,  # no slack\n",
     "        3000,  # vehicle maximum travel distance\n",
     "        True,  # start cumul to zero\n",
-    "        dimension_name)\n",
+    "        dimension_name,\n",
+    "    )\n",
     "    distance_dimension = routing.GetDimensionOrDie(dimension_name)\n",
     "    distance_dimension.SetGlobalSpanCostCoefficient(100)\n",
     "\n",
     "    # Define Transportation Requests.\n",
-    "    for request in data['pickups_deliveries']:\n",
+    "    for request in data[\"pickups_deliveries\"]:\n",
     "        pickup_index = manager.NodeToIndex(request[0])\n",
     "        delivery_index = manager.NodeToIndex(request[1])\n",
     "        routing.AddPickupAndDelivery(pickup_index, delivery_index)\n",
     "        routing.solver().Add(\n",
-    "            routing.VehicleVar(pickup_index) == routing.VehicleVar(\n",
-    "                delivery_index))\n",
+    "            routing.VehicleVar(pickup_index) == routing.VehicleVar(delivery_index)\n",
+    "        )\n",
     "        routing.solver().Add(\n",
-    "            distance_dimension.CumulVar(pickup_index) <=\n",
-    "            distance_dimension.CumulVar(delivery_index))\n",
+    "            distance_dimension.CumulVar(pickup_index)\n",
+    "            <= distance_dimension.CumulVar(delivery_index)\n",
+    "        )\n",
     "    routing.SetPickupAndDeliveryPolicyOfAllVehicles(\n",
-    "        pywrapcp.RoutingModel.PICKUP_AND_DELIVERY_FIFO)\n",
+    "        pywrapcp.RoutingModel.PICKUP_AND_DELIVERY_FIFO\n",
+    "    )\n",
     "\n",
     "    # Setting first solution heuristic.\n",
     "    search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n",
     "    search_parameters.first_solution_strategy = (\n",
-    "        routing_enums_pb2.FirstSolutionStrategy.PARALLEL_CHEAPEST_INSERTION)\n",
+    "        routing_enums_pb2.FirstSolutionStrategy.PARALLEL_CHEAPEST_INSERTION\n",
+    "    )\n",
     "\n",
     "    # Solve the problem.\n",
     "    assignment = routing.SolveWithParameters(search_parameters)\n",
diff --git a/examples/notebook/constraint_solver/vrp_pickup_delivery_lifo.ipynb b/examples/notebook/constraint_solver/vrp_pickup_delivery_lifo.ipynb
index d22849ae68..e43ef4d199 100644
--- a/examples/notebook/constraint_solver/vrp_pickup_delivery_lifo.ipynb
+++ b/examples/notebook/constraint_solver/vrp_pickup_delivery_lifo.ipynb
@@ -90,77 +90,28 @@
     "def create_data_model():\n",
     "    \"\"\"Stores the data for the problem.\"\"\"\n",
     "    data = {}\n",
-    "    data['distance_matrix'] = [\n",
-    "        [\n",
-    "            0, 548, 776, 696, 582, 274, 502, 194, 308, 194, 536, 502, 388, 354,\n",
-    "            468, 776, 662\n",
-    "        ],\n",
-    "        [\n",
-    "            548, 0, 684, 308, 194, 502, 730, 354, 696, 742, 1084, 594, 480, 674,\n",
-    "            1016, 868, 1210\n",
-    "        ],\n",
-    "        [\n",
-    "            776, 684, 0, 992, 878, 502, 274, 810, 468, 742, 400, 1278, 1164,\n",
-    "            1130, 788, 1552, 754\n",
-    "        ],\n",
-    "        [\n",
-    "            696, 308, 992, 0, 114, 650, 878, 502, 844, 890, 1232, 514, 628, 822,\n",
-    "            1164, 560, 1358\n",
-    "        ],\n",
-    "        [\n",
-    "            582, 194, 878, 114, 0, 536, 764, 388, 730, 776, 1118, 400, 514, 708,\n",
-    "            1050, 674, 1244\n",
-    "        ],\n",
-    "        [\n",
-    "            274, 502, 502, 650, 536, 0, 228, 308, 194, 240, 582, 776, 662, 628,\n",
-    "            514, 1050, 708\n",
-    "        ],\n",
-    "        [\n",
-    "            502, 730, 274, 878, 764, 228, 0, 536, 194, 468, 354, 1004, 890, 856,\n",
-    "            514, 1278, 480\n",
-    "        ],\n",
-    "        [\n",
-    "            194, 354, 810, 502, 388, 308, 536, 0, 342, 388, 730, 468, 354, 320,\n",
-    "            662, 742, 856\n",
-    "        ],\n",
-    "        [\n",
-    "            308, 696, 468, 844, 730, 194, 194, 342, 0, 274, 388, 810, 696, 662,\n",
-    "            320, 1084, 514\n",
-    "        ],\n",
-    "        [\n",
-    "            194, 742, 742, 890, 776, 240, 468, 388, 274, 0, 342, 536, 422, 388,\n",
-    "            274, 810, 468\n",
-    "        ],\n",
-    "        [\n",
-    "            536, 1084, 400, 1232, 1118, 582, 354, 730, 388, 342, 0, 878, 764,\n",
-    "            730, 388, 1152, 354\n",
-    "        ],\n",
-    "        [\n",
-    "            502, 594, 1278, 514, 400, 776, 1004, 468, 810, 536, 878, 0, 114,\n",
-    "            308, 650, 274, 844\n",
-    "        ],\n",
-    "        [\n",
-    "            388, 480, 1164, 628, 514, 662, 890, 354, 696, 422, 764, 114, 0, 194,\n",
-    "            536, 388, 730\n",
-    "        ],\n",
-    "        [\n",
-    "            354, 674, 1130, 822, 708, 628, 856, 320, 662, 388, 730, 308, 194, 0,\n",
-    "            342, 422, 536\n",
-    "        ],\n",
-    "        [\n",
-    "            468, 1016, 788, 1164, 1050, 514, 514, 662, 320, 274, 388, 650, 536,\n",
-    "            342, 0, 764, 194\n",
-    "        ],\n",
-    "        [\n",
-    "            776, 868, 1552, 560, 674, 1050, 1278, 742, 1084, 810, 1152, 274,\n",
-    "            388, 422, 764, 0, 798\n",
-    "        ],\n",
-    "        [\n",
-    "            662, 1210, 754, 1358, 1244, 708, 480, 856, 514, 468, 354, 844, 730,\n",
-    "            536, 194, 798, 0\n",
-    "        ],\n",
+    "    data[\"distance_matrix\"] = [\n",
+    "        # fmt: off\n",
+    "      [0, 548, 776, 696, 582, 274, 502, 194, 308, 194, 536, 502, 388, 354, 468, 776, 662],\n",
+    "      [548, 0, 684, 308, 194, 502, 730, 354, 696, 742, 1084, 594, 480, 674, 1016, 868, 1210],\n",
+    "      [776, 684, 0, 992, 878, 502, 274, 810, 468, 742, 400, 1278, 1164, 1130, 788, 1552, 754],\n",
+    "      [696, 308, 992, 0, 114, 650, 878, 502, 844, 890, 1232, 514, 628, 822, 1164, 560, 1358],\n",
+    "      [582, 194, 878, 114, 0, 536, 764, 388, 730, 776, 1118, 400, 514, 708, 1050, 674, 1244],\n",
+    "      [274, 502, 502, 650, 536, 0, 228, 308, 194, 240, 582, 776, 662, 628, 514, 1050, 708],\n",
+    "      [502, 730, 274, 878, 764, 228, 0, 536, 194, 468, 354, 1004, 890, 856, 514, 1278, 480],\n",
+    "      [194, 354, 810, 502, 388, 308, 536, 0, 342, 388, 730, 468, 354, 320, 662, 742, 856],\n",
+    "      [308, 696, 468, 844, 730, 194, 194, 342, 0, 274, 388, 810, 696, 662, 320, 1084, 514],\n",
+    "      [194, 742, 742, 890, 776, 240, 468, 388, 274, 0, 342, 536, 422, 388, 274, 810, 468],\n",
+    "      [536, 1084, 400, 1232, 1118, 582, 354, 730, 388, 342, 0, 878, 764, 730, 388, 1152, 354],\n",
+    "      [502, 594, 1278, 514, 400, 776, 1004, 468, 810, 536, 878, 0, 114, 308, 650, 274, 844],\n",
+    "      [388, 480, 1164, 628, 514, 662, 890, 354, 696, 422, 764, 114, 0, 194, 536, 388, 730],\n",
+    "      [354, 674, 1130, 822, 708, 628, 856, 320, 662, 388, 730, 308, 194, 0, 342, 422, 536],\n",
+    "      [468, 1016, 788, 1164, 1050, 514, 514, 662, 320, 274, 388, 650, 536, 342, 0, 764, 194],\n",
+    "      [776, 868, 1552, 560, 674, 1050, 1278, 742, 1084, 810, 1152, 274, 388, 422, 764, 0, 798],\n",
+    "      [662, 1210, 754, 1358, 1244, 708, 480, 856, 514, 468, 354, 844, 730, 536, 194, 798, 0],\n",
+    "        # fmt: on\n",
     "    ]\n",
-    "    data['pickups_deliveries'] = [\n",
+    "    data[\"pickups_deliveries\"] = [\n",
     "        [1, 6],\n",
     "        [2, 10],\n",
     "        [4, 3],\n",
@@ -170,30 +121,31 @@
     "        [13, 12],\n",
     "        [16, 14],\n",
     "    ]\n",
-    "    data['num_vehicles'] = 4\n",
-    "    data['depot'] = 0\n",
+    "    data[\"num_vehicles\"] = 4\n",
+    "    data[\"depot\"] = 0\n",
     "    return data\n",
     "\n",
     "\n",
     "def print_solution(data, manager, routing, assignment):\n",
     "    \"\"\"Prints assignment on console.\"\"\"\n",
-    "    print(f'Objective: {assignment.ObjectiveValue()}')\n",
+    "    print(f\"Objective: {assignment.ObjectiveValue()}\")\n",
     "    total_distance = 0\n",
-    "    for vehicle_id in range(data['num_vehicles']):\n",
+    "    for vehicle_id in range(data[\"num_vehicles\"]):\n",
     "        index = routing.Start(vehicle_id)\n",
-    "        plan_output = f'Route for vehicle {vehicle_id}:\\n'\n",
+    "        plan_output = f\"Route for vehicle {vehicle_id}:\\n\"\n",
     "        route_distance = 0\n",
     "        while not routing.IsEnd(index):\n",
-    "            plan_output += f' {manager.IndexToNode(index)} -> '\n",
+    "            plan_output += f\" {manager.IndexToNode(index)} -> \"\n",
     "            previous_index = index\n",
     "            index = assignment.Value(routing.NextVar(index))\n",
     "            route_distance += routing.GetArcCostForVehicle(\n",
-    "                previous_index, index, vehicle_id)\n",
-    "        plan_output += f'{manager.IndexToNode(index)}\\n'\n",
-    "        plan_output += f'Distance of the route: {route_distance}m\\n'\n",
+    "                previous_index, index, vehicle_id\n",
+    "            )\n",
+    "        plan_output += f\"{manager.IndexToNode(index)}\\n\"\n",
+    "        plan_output += f\"Distance of the route: {route_distance}m\\n\"\n",
     "        print(plan_output)\n",
     "        total_distance += route_distance\n",
-    "    print(f'Total Distance of all routes: {total_distance}m')\n",
+    "    print(f\"Total Distance of all routes: {total_distance}m\")\n",
     "\n",
     "\n",
     "def main():\n",
@@ -202,8 +154,9 @@
     "    data = create_data_model()\n",
     "\n",
     "    # Create the routing index manager.\n",
-    "    manager = pywrapcp.RoutingIndexManager(len(data['distance_matrix']),\n",
-    "                                           data['num_vehicles'], data['depot'])\n",
+    "    manager = pywrapcp.RoutingIndexManager(\n",
+    "        len(data[\"distance_matrix\"]), data[\"num_vehicles\"], data[\"depot\"]\n",
+    "    )\n",
     "\n",
     "    # Create Routing Model.\n",
     "    routing = pywrapcp.RoutingModel(manager)\n",
@@ -215,40 +168,44 @@
     "        # Convert from routing variable Index to distance matrix NodeIndex.\n",
     "        from_node = manager.IndexToNode(from_index)\n",
     "        to_node = manager.IndexToNode(to_index)\n",
-    "        return data['distance_matrix'][from_node][to_node]\n",
+    "        return data[\"distance_matrix\"][from_node][to_node]\n",
     "\n",
     "    transit_callback_index = routing.RegisterTransitCallback(distance_callback)\n",
     "    routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)\n",
     "\n",
     "    # Add Distance constraint.\n",
-    "    dimension_name = 'Distance'\n",
+    "    dimension_name = \"Distance\"\n",
     "    routing.AddDimension(\n",
     "        transit_callback_index,\n",
     "        0,  # no slack\n",
     "        3000,  # vehicle maximum travel distance\n",
     "        True,  # start cumul to zero\n",
-    "        dimension_name)\n",
+    "        dimension_name,\n",
+    "    )\n",
     "    distance_dimension = routing.GetDimensionOrDie(dimension_name)\n",
     "    distance_dimension.SetGlobalSpanCostCoefficient(100)\n",
     "\n",
     "    # Define Transportation Requests.\n",
-    "    for request in data['pickups_deliveries']:\n",
+    "    for request in data[\"pickups_deliveries\"]:\n",
     "        pickup_index = manager.NodeToIndex(request[0])\n",
     "        delivery_index = manager.NodeToIndex(request[1])\n",
     "        routing.AddPickupAndDelivery(pickup_index, delivery_index)\n",
     "        routing.solver().Add(\n",
-    "            routing.VehicleVar(pickup_index) == routing.VehicleVar(\n",
-    "                delivery_index))\n",
+    "            routing.VehicleVar(pickup_index) == routing.VehicleVar(delivery_index)\n",
+    "        )\n",
     "        routing.solver().Add(\n",
-    "            distance_dimension.CumulVar(pickup_index) <=\n",
-    "            distance_dimension.CumulVar(delivery_index))\n",
+    "            distance_dimension.CumulVar(pickup_index)\n",
+    "            <= distance_dimension.CumulVar(delivery_index)\n",
+    "        )\n",
     "    routing.SetPickupAndDeliveryPolicyOfAllVehicles(\n",
-    "        pywrapcp.RoutingModel.PICKUP_AND_DELIVERY_LIFO)\n",
+    "        pywrapcp.RoutingModel.PICKUP_AND_DELIVERY_LIFO\n",
+    "    )\n",
     "\n",
     "    # Setting first solution heuristic.\n",
     "    search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n",
     "    search_parameters.first_solution_strategy = (\n",
-    "        routing_enums_pb2.FirstSolutionStrategy.PARALLEL_CHEAPEST_INSERTION)\n",
+    "        routing_enums_pb2.FirstSolutionStrategy.PARALLEL_CHEAPEST_INSERTION\n",
+    "    )\n",
     "\n",
     "    # Solve the problem.\n",
     "    assignment = routing.SolveWithParameters(search_parameters)\n",
diff --git a/examples/notebook/constraint_solver/vrp_resources.ipynb b/examples/notebook/constraint_solver/vrp_resources.ipynb
index 036cdff0d1..b14d3fe12f 100644
--- a/examples/notebook/constraint_solver/vrp_resources.ipynb
+++ b/examples/notebook/constraint_solver/vrp_resources.ipynb
@@ -90,7 +90,7 @@
     "def create_data_model():\n",
     "    \"\"\"Stores the data for the problem.\"\"\"\n",
     "    data = {}\n",
-    "    data['time_matrix'] = [\n",
+    "    data[\"time_matrix\"] = [\n",
     "        [0, 6, 9, 8, 7, 3, 6, 2, 3, 2, 6, 6, 4, 4, 5, 9, 7],\n",
     "        [6, 0, 8, 3, 2, 6, 8, 4, 8, 8, 13, 7, 5, 8, 12, 10, 14],\n",
     "        [9, 8, 0, 11, 10, 6, 3, 9, 5, 8, 4, 15, 14, 13, 9, 18, 9],\n",
@@ -109,7 +109,7 @@
     "        [9, 10, 18, 6, 8, 12, 15, 8, 13, 9, 13, 3, 4, 5, 9, 0, 9],\n",
     "        [7, 14, 9, 16, 14, 8, 5, 10, 6, 5, 4, 10, 8, 6, 2, 9, 0],\n",
     "    ]\n",
-    "    data['time_windows'] = [\n",
+    "    data[\"time_windows\"] = [\n",
     "        (0, 5),  # depot\n",
     "        (7, 12),  # 1\n",
     "        (10, 15),  # 2\n",
@@ -128,37 +128,39 @@
     "        (10, 15),  # 15\n",
     "        (5, 15),  # 16\n",
     "    ]\n",
-    "    data['num_vehicles'] = 4\n",
-    "    data['vehicle_load_time'] = 5\n",
-    "    data['vehicle_unload_time'] = 5\n",
-    "    data['depot_capacity'] = 2\n",
-    "    data['depot'] = 0\n",
+    "    data[\"num_vehicles\"] = 4\n",
+    "    data[\"vehicle_load_time\"] = 5\n",
+    "    data[\"vehicle_unload_time\"] = 5\n",
+    "    data[\"depot_capacity\"] = 2\n",
+    "    data[\"depot\"] = 0\n",
     "    return data\n",
     "\n",
     "\n",
     "def print_solution(data, manager, routing, solution):\n",
     "    \"\"\"Prints solution on console.\"\"\"\n",
-    "    print(f'Objective: {solution.ObjectiveValue()}')\n",
-    "    time_dimension = routing.GetDimensionOrDie('Time')\n",
+    "    print(f\"Objective: {solution.ObjectiveValue()}\")\n",
+    "    time_dimension = routing.GetDimensionOrDie(\"Time\")\n",
     "    total_time = 0\n",
-    "    for vehicle_id in range(data['num_vehicles']):\n",
+    "    for vehicle_id in range(data[\"num_vehicles\"]):\n",
     "        index = routing.Start(vehicle_id)\n",
-    "        plan_output = f'Route for vehicle {vehicle_id}:\\n'\n",
+    "        plan_output = f\"Route for vehicle {vehicle_id}:\\n\"\n",
     "        while not routing.IsEnd(index):\n",
     "            time_var = time_dimension.CumulVar(index)\n",
     "            plan_output += (\n",
-    "                f'{manager.IndexToNode(index)}'\n",
-    "                f' Time({solution.Min(time_var)}, {solution.Max(time_var)})'\n",
-    "                ' -> ')\n",
+    "                f\"{manager.IndexToNode(index)}\"\n",
+    "                f\" Time({solution.Min(time_var)}, {solution.Max(time_var)})\"\n",
+    "                \" -> \"\n",
+    "            )\n",
     "            index = solution.Value(routing.NextVar(index))\n",
     "        time_var = time_dimension.CumulVar(index)\n",
     "        plan_output += (\n",
-    "            f'{manager.IndexToNode(index)}'\n",
-    "            f' Time({solution.Min(time_var)},{solution.Max(time_var)})\\n')\n",
-    "        plan_output += f'Time of the route: {solution.Min(time_var)}min\\n'\n",
+    "            f\"{manager.IndexToNode(index)}\"\n",
+    "            f\" Time({solution.Min(time_var)},{solution.Max(time_var)})\\n\"\n",
+    "        )\n",
+    "        plan_output += f\"Time of the route: {solution.Min(time_var)}min\\n\"\n",
     "        print(plan_output)\n",
     "        total_time += solution.Min(time_var)\n",
-    "    print(f'Total time of all routes: {total_time}min')\n",
+    "    print(f\"Total time of all routes: {total_time}min\")\n",
     "\n",
     "\n",
     "def main():\n",
@@ -167,20 +169,20 @@
     "    data = create_data_model()\n",
     "\n",
     "    # Create the routing index manager.\n",
-    "    manager = pywrapcp.RoutingIndexManager(len(data['time_matrix']),\n",
-    "                                           data['num_vehicles'], data['depot'])\n",
+    "    manager = pywrapcp.RoutingIndexManager(\n",
+    "        len(data[\"time_matrix\"]), data[\"num_vehicles\"], data[\"depot\"]\n",
+    "    )\n",
     "\n",
     "    # Create Routing Model.\n",
     "    routing = pywrapcp.RoutingModel(manager)\n",
     "\n",
-    "\n",
     "    # Create and register a transit callback.\n",
     "    def time_callback(from_index, to_index):\n",
     "        \"\"\"Returns the travel time between the two nodes.\"\"\"\n",
     "        # Convert from routing variable Index to time matrix NodeIndex.\n",
     "        from_node = manager.IndexToNode(from_index)\n",
     "        to_node = manager.IndexToNode(to_index)\n",
-    "        return data['time_matrix'][from_node][to_node]\n",
+    "        return data[\"time_matrix\"][from_node][to_node]\n",
     "\n",
     "    transit_callback_index = routing.RegisterTransitCallback(time_callback)\n",
     "\n",
@@ -188,57 +190,66 @@
     "    routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)\n",
     "\n",
     "    # Add Time Windows constraint.\n",
-    "    time = 'Time'\n",
+    "    time = \"Time\"\n",
     "    routing.AddDimension(\n",
     "        transit_callback_index,\n",
     "        60,  # allow waiting time\n",
     "        60,  # maximum time per vehicle\n",
     "        False,  # Don't force start cumul to zero.\n",
-    "        time)\n",
+    "        time,\n",
+    "    )\n",
     "    time_dimension = routing.GetDimensionOrDie(time)\n",
     "    # Add time window constraints for each location except depot.\n",
-    "    for location_idx, time_window in enumerate(data['time_windows']):\n",
+    "    for location_idx, time_window in enumerate(data[\"time_windows\"]):\n",
     "        if location_idx == 0:\n",
     "            continue\n",
     "        index = manager.NodeToIndex(location_idx)\n",
     "        time_dimension.CumulVar(index).SetRange(time_window[0], time_window[1])\n",
     "    # Add time window constraints for each vehicle start node.\n",
-    "    for vehicle_id in range(data['num_vehicles']):\n",
+    "    for vehicle_id in range(data[\"num_vehicles\"]):\n",
     "        index = routing.Start(vehicle_id)\n",
-    "        time_dimension.CumulVar(index).SetRange(data['time_windows'][0][0],\n",
-    "                                                data['time_windows'][0][1])\n",
+    "        time_dimension.CumulVar(index).SetRange(\n",
+    "            data[\"time_windows\"][0][0], data[\"time_windows\"][0][1]\n",
+    "        )\n",
     "\n",
     "    # Add resource constraints at the depot.\n",
     "    solver = routing.solver()\n",
     "    intervals = []\n",
-    "    for i in range(data['num_vehicles']):\n",
+    "    for i in range(data[\"num_vehicles\"]):\n",
     "        # Add time windows at start of routes\n",
     "        intervals.append(\n",
     "            solver.FixedDurationIntervalVar(\n",
     "                time_dimension.CumulVar(routing.Start(i)),\n",
-    "                data['vehicle_load_time'], 'depot_interval'))\n",
+    "                data[\"vehicle_load_time\"],\n",
+    "                \"depot_interval\",\n",
+    "            )\n",
+    "        )\n",
     "        # Add time windows at end of routes.\n",
     "        intervals.append(\n",
     "            solver.FixedDurationIntervalVar(\n",
     "                time_dimension.CumulVar(routing.End(i)),\n",
-    "                data['vehicle_unload_time'], 'depot_interval'))\n",
+    "                data[\"vehicle_unload_time\"],\n",
+    "                \"depot_interval\",\n",
+    "            )\n",
+    "        )\n",
     "\n",
     "    depot_usage = [1 for i in range(len(intervals))]\n",
     "    solver.Add(\n",
-    "        solver.Cumulative(intervals, depot_usage, data['depot_capacity'],\n",
-    "                          'depot'))\n",
+    "        solver.Cumulative(intervals, depot_usage, data[\"depot_capacity\"], \"depot\")\n",
+    "    )\n",
     "\n",
     "    # Instantiate route start and end times to produce feasible times.\n",
-    "    for i in range(data['num_vehicles']):\n",
+    "    for i in range(data[\"num_vehicles\"]):\n",
     "        routing.AddVariableMinimizedByFinalizer(\n",
-    "            time_dimension.CumulVar(routing.Start(i)))\n",
-    "        routing.AddVariableMinimizedByFinalizer(\n",
-    "            time_dimension.CumulVar(routing.End(i)))\n",
+    "            time_dimension.CumulVar(routing.Start(i))\n",
+    "        )\n",
+    "        routing.AddVariableMinimizedByFinalizer(time_dimension.CumulVar(routing.End(i)))\n",
     "\n",
     "    # Setting first solution heuristic.\n",
     "    search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n",
     "    search_parameters.first_solution_strategy = (\n",
-    "        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)\n",
+    "        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC\n",
+    "    )\n",
     "\n",
     "    # Solve the problem.\n",
     "    solution = routing.SolveWithParameters(search_parameters)\n",
@@ -247,7 +258,7 @@
     "    if solution:\n",
     "        print_solution(data, manager, routing, solution)\n",
     "    else:\n",
-    "        print('No solution found !')\n",
+    "        print(\"No solution found !\")\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/constraint_solver/vrp_solution_callback.ipynb b/examples/notebook/constraint_solver/vrp_solution_callback.ipynb
index 50b8b744ce..0295a28d4e 100644
--- a/examples/notebook/constraint_solver/vrp_solution_callback.ipynb
+++ b/examples/notebook/constraint_solver/vrp_solution_callback.ipynb
@@ -98,109 +98,66 @@
     "def create_data_model():\n",
     "    \"\"\"Stores the data for the problem.\"\"\"\n",
     "    data = {}\n",
-    "    data['distance_matrix'] = [\n",
-    "        [\n",
-    "            0, 548, 776, 696, 582, 274, 502, 194, 308, 194, 536, 502, 388, 354,\n",
-    "            468, 776, 662\n",
-    "        ],\n",
-    "        [\n",
-    "            548, 0, 684, 308, 194, 502, 730, 354, 696, 742, 1084, 594, 480, 674,\n",
-    "            1016, 868, 1210\n",
-    "        ],\n",
-    "        [\n",
-    "            776, 684, 0, 992, 878, 502, 274, 810, 468, 742, 400, 1278, 1164,\n",
-    "            1130, 788, 1552, 754\n",
-    "        ],\n",
-    "        [\n",
-    "            696, 308, 992, 0, 114, 650, 878, 502, 844, 890, 1232, 514, 628, 822,\n",
-    "            1164, 560, 1358\n",
-    "        ],\n",
-    "        [\n",
-    "            582, 194, 878, 114, 0, 536, 764, 388, 730, 776, 1118, 400, 514, 708,\n",
-    "            1050, 674, 1244\n",
-    "        ],\n",
-    "        [\n",
-    "            274, 502, 502, 650, 536, 0, 228, 308, 194, 240, 582, 776, 662, 628,\n",
-    "            514, 1050, 708\n",
-    "        ],\n",
-    "        [\n",
-    "            502, 730, 274, 878, 764, 228, 0, 536, 194, 468, 354, 1004, 890, 856,\n",
-    "            514, 1278, 480\n",
-    "        ],\n",
-    "        [\n",
-    "            194, 354, 810, 502, 388, 308, 536, 0, 342, 388, 730, 468, 354, 320,\n",
-    "            662, 742, 856\n",
-    "        ],\n",
-    "        [\n",
-    "            308, 696, 468, 844, 730, 194, 194, 342, 0, 274, 388, 810, 696, 662,\n",
-    "            320, 1084, 514\n",
-    "        ],\n",
-    "        [\n",
-    "            194, 742, 742, 890, 776, 240, 468, 388, 274, 0, 342, 536, 422, 388,\n",
-    "            274, 810, 468\n",
-    "        ],\n",
-    "        [\n",
-    "            536, 1084, 400, 1232, 1118, 582, 354, 730, 388, 342, 0, 878, 764,\n",
-    "            730, 388, 1152, 354\n",
-    "        ],\n",
-    "        [\n",
-    "            502, 594, 1278, 514, 400, 776, 1004, 468, 810, 536, 878, 0, 114,\n",
-    "            308, 650, 274, 844\n",
-    "        ],\n",
-    "        [\n",
-    "            388, 480, 1164, 628, 514, 662, 890, 354, 696, 422, 764, 114, 0, 194,\n",
-    "            536, 388, 730\n",
-    "        ],\n",
-    "        [\n",
-    "            354, 674, 1130, 822, 708, 628, 856, 320, 662, 388, 730, 308, 194, 0,\n",
-    "            342, 422, 536\n",
-    "        ],\n",
-    "        [\n",
-    "            468, 1016, 788, 1164, 1050, 514, 514, 662, 320, 274, 388, 650, 536,\n",
-    "            342, 0, 764, 194\n",
-    "        ],\n",
-    "        [\n",
-    "            776, 868, 1552, 560, 674, 1050, 1278, 742, 1084, 810, 1152, 274,\n",
-    "            388, 422, 764, 0, 798\n",
-    "        ],\n",
-    "        [\n",
-    "            662, 1210, 754, 1358, 1244, 708, 480, 856, 514, 468, 354, 844, 730,\n",
-    "            536, 194, 798, 0\n",
-    "        ],\n",
+    "    data[\"distance_matrix\"] = [\n",
+    "        # fmt: off\n",
+    "      [0, 548, 776, 696, 582, 274, 502, 194, 308, 194, 536, 502, 388, 354, 468, 776, 662],\n",
+    "      [548, 0, 684, 308, 194, 502, 730, 354, 696, 742, 1084, 594, 480, 674, 1016, 868, 1210],\n",
+    "      [776, 684, 0, 992, 878, 502, 274, 810, 468, 742, 400, 1278, 1164, 1130, 788, 1552, 754],\n",
+    "      [696, 308, 992, 0, 114, 650, 878, 502, 844, 890, 1232, 514, 628, 822, 1164, 560, 1358],\n",
+    "      [582, 194, 878, 114, 0, 536, 764, 388, 730, 776, 1118, 400, 514, 708, 1050, 674, 1244],\n",
+    "      [274, 502, 502, 650, 536, 0, 228, 308, 194, 240, 582, 776, 662, 628, 514, 1050, 708],\n",
+    "      [502, 730, 274, 878, 764, 228, 0, 536, 194, 468, 354, 1004, 890, 856, 514, 1278, 480],\n",
+    "      [194, 354, 810, 502, 388, 308, 536, 0, 342, 388, 730, 468, 354, 320, 662, 742, 856],\n",
+    "      [308, 696, 468, 844, 730, 194, 194, 342, 0, 274, 388, 810, 696, 662, 320, 1084, 514],\n",
+    "      [194, 742, 742, 890, 776, 240, 468, 388, 274, 0, 342, 536, 422, 388, 274, 810, 468],\n",
+    "      [536, 1084, 400, 1232, 1118, 582, 354, 730, 388, 342, 0, 878, 764, 730, 388, 1152, 354],\n",
+    "      [502, 594, 1278, 514, 400, 776, 1004, 468, 810, 536, 878, 0, 114, 308, 650, 274, 844],\n",
+    "      [388, 480, 1164, 628, 514, 662, 890, 354, 696, 422, 764, 114, 0, 194, 536, 388, 730],\n",
+    "      [354, 674, 1130, 822, 708, 628, 856, 320, 662, 388, 730, 308, 194, 0, 342, 422, 536],\n",
+    "      [468, 1016, 788, 1164, 1050, 514, 514, 662, 320, 274, 388, 650, 536, 342, 0, 764, 194],\n",
+    "      [776, 868, 1552, 560, 674, 1050, 1278, 742, 1084, 810, 1152, 274, 388, 422, 764, 0, 798],\n",
+    "      [662, 1210, 754, 1358, 1244, 708, 480, 856, 514, 468, 354, 844, 730, 536, 194, 798, 0],\n",
+    "        # fmt: on\n",
     "    ]\n",
-    "    data['num_vehicles'] = 4\n",
-    "    data['depot'] = 0\n",
+    "    data[\"num_vehicles\"] = 4\n",
+    "    data[\"depot\"] = 0\n",
     "    return data\n",
     "\n",
     "\n",
-    "def print_solution(routing_manager: pywrapcp.RoutingIndexManager,\n",
-    "                   routing_model: pywrapcp.RoutingModel):\n",
+    "def print_solution(\n",
+    "    routing_manager: pywrapcp.RoutingIndexManager, routing_model: pywrapcp.RoutingModel\n",
+    "):\n",
     "    \"\"\"Prints solution on console.\"\"\"\n",
-    "    print('################')\n",
-    "    print(f'Solution objective: {routing_model.CostVar().Value()}')\n",
+    "    print(\"################\")\n",
+    "    print(f\"Solution objective: {routing_model.CostVar().Value()}\")\n",
     "    total_distance = 0\n",
     "    for vehicle_id in range(routing_manager.GetNumberOfVehicles()):\n",
     "        index = routing_model.Start(vehicle_id)\n",
-    "        plan_output = f'Route for vehicle {vehicle_id}:\\n'\n",
+    "        plan_output = f\"Route for vehicle {vehicle_id}:\\n\"\n",
     "        route_distance = 0\n",
     "        while not routing_model.IsEnd(index):\n",
-    "            plan_output += f' {routing_manager.IndexToNode(index)} ->'\n",
+    "            plan_output += f\" {routing_manager.IndexToNode(index)} ->\"\n",
     "            previous_index = index\n",
     "            index = routing_model.NextVar(index).Value()\n",
     "            route_distance += routing_model.GetArcCostForVehicle(\n",
-    "                previous_index, index, vehicle_id)\n",
-    "        plan_output += f' {routing_manager.IndexToNode(index)}\\n'\n",
-    "        plan_output += f'Distance of the route: {route_distance}m\\n'\n",
+    "                previous_index, index, vehicle_id\n",
+    "            )\n",
+    "        plan_output += f\" {routing_manager.IndexToNode(index)}\\n\"\n",
+    "        plan_output += f\"Distance of the route: {route_distance}m\\n\"\n",
     "        print(plan_output)\n",
     "        total_distance += route_distance\n",
-    "    print(f'Total Distance of all routes: {total_distance}m')\n",
+    "    print(f\"Total Distance of all routes: {total_distance}m\")\n",
     "\n",
     "\n",
     "class SolutionCallback:\n",
     "    \"\"\"Create a solution callback.\"\"\"\n",
     "\n",
-    "    def __init__(self, manager: pywrapcp.RoutingIndexManager,\n",
-    "                 model: pywrapcp.RoutingModel, limit: int):\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        manager: pywrapcp.RoutingIndexManager,\n",
+    "        model: pywrapcp.RoutingModel,\n",
+    "        limit: int,\n",
+    "    ):\n",
     "        self._routing_manager = manager\n",
     "        self._routing_model = model\n",
     "        self._counter = 0\n",
@@ -223,9 +180,9 @@
     "    data = create_data_model()\n",
     "\n",
     "    # Create the routing index manager.\n",
-    "    routing_manager = pywrapcp.RoutingIndexManager(len(data['distance_matrix']),\n",
-    "                                                   data['num_vehicles'],\n",
-    "                                                   data['depot'])\n",
+    "    routing_manager = pywrapcp.RoutingIndexManager(\n",
+    "        len(data[\"distance_matrix\"]), data[\"num_vehicles\"], data[\"depot\"]\n",
+    "    )\n",
     "\n",
     "    # Create Routing Model.\n",
     "    routing_model = pywrapcp.RoutingModel(routing_manager)\n",
@@ -237,22 +194,22 @@
     "        # Convert from routing variable Index to distance matrix NodeIndex.\n",
     "        from_node = routing_manager.IndexToNode(from_index)\n",
     "        to_node = routing_manager.IndexToNode(to_index)\n",
-    "        return data['distance_matrix'][from_node][to_node]\n",
+    "        return data[\"distance_matrix\"][from_node][to_node]\n",
     "\n",
-    "    transit_callback_index = routing_model.RegisterTransitCallback(\n",
-    "        distance_callback)\n",
+    "    transit_callback_index = routing_model.RegisterTransitCallback(distance_callback)\n",
     "\n",
     "    # Define cost of each arc.\n",
     "    routing_model.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)\n",
     "\n",
     "    # Add Distance constraint.\n",
-    "    dimension_name = 'Distance'\n",
+    "    dimension_name = \"Distance\"\n",
     "    routing_model.AddDimension(\n",
     "        transit_callback_index,\n",
     "        0,  # no slack\n",
     "        3000,  # vehicle maximum travel distance\n",
     "        True,  # start cumul to zero\n",
-    "        dimension_name)\n",
+    "        dimension_name,\n",
+    "    )\n",
     "    distance_dimension = routing_model.GetDimensionOrDie(dimension_name)\n",
     "    distance_dimension.SetGlobalSpanCostCoefficient(100)\n",
     "\n",
@@ -263,9 +220,11 @@
     "    # Setting first solution heuristic.\n",
     "    search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n",
     "    search_parameters.first_solution_strategy = (\n",
-    "        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)\n",
+    "        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC\n",
+    "    )\n",
     "    search_parameters.local_search_metaheuristic = (\n",
-    "        routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH)\n",
+    "        routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH\n",
+    "    )\n",
     "    search_parameters.time_limit.FromSeconds(5)\n",
     "\n",
     "    # Solve the problem.\n",
@@ -273,9 +232,9 @@
     "\n",
     "    # Print solution on console.\n",
     "    if solution:\n",
-    "        print(f'Best objective: {solution_callback.objectives[-1]}')\n",
+    "        print(f\"Best objective: {solution_callback.objectives[-1]}\")\n",
     "    else:\n",
-    "        print('No solution found !')\n",
+    "        print(\"No solution found !\")\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/constraint_solver/vrp_starts_ends.ipynb b/examples/notebook/constraint_solver/vrp_starts_ends.ipynb
index 5a994a4875..77c566b60f 100644
--- a/examples/notebook/constraint_solver/vrp_starts_ends.ipynb
+++ b/examples/notebook/constraint_solver/vrp_starts_ends.ipynb
@@ -90,101 +90,53 @@
     "def create_data_model():\n",
     "    \"\"\"Stores the data for the problem.\"\"\"\n",
     "    data = {}\n",
-    "    data['distance_matrix'] = [\n",
-    "        [\n",
-    "            0, 548, 776, 696, 582, 274, 502, 194, 308, 194, 536, 502, 388, 354,\n",
-    "            468, 776, 662\n",
-    "        ],\n",
-    "        [\n",
-    "            548, 0, 684, 308, 194, 502, 730, 354, 696, 742, 1084, 594, 480, 674,\n",
-    "            1016, 868, 1210\n",
-    "        ],\n",
-    "        [\n",
-    "            776, 684, 0, 992, 878, 502, 274, 810, 468, 742, 400, 1278, 1164,\n",
-    "            1130, 788, 1552, 754\n",
-    "        ],\n",
-    "        [\n",
-    "            696, 308, 992, 0, 114, 650, 878, 502, 844, 890, 1232, 514, 628, 822,\n",
-    "            1164, 560, 1358\n",
-    "        ],\n",
-    "        [\n",
-    "            582, 194, 878, 114, 0, 536, 764, 388, 730, 776, 1118, 400, 514, 708,\n",
-    "            1050, 674, 1244\n",
-    "        ],\n",
-    "        [\n",
-    "            274, 502, 502, 650, 536, 0, 228, 308, 194, 240, 582, 776, 662, 628,\n",
-    "            514, 1050, 708\n",
-    "        ],\n",
-    "        [\n",
-    "            502, 730, 274, 878, 764, 228, 0, 536, 194, 468, 354, 1004, 890, 856,\n",
-    "            514, 1278, 480\n",
-    "        ],\n",
-    "        [\n",
-    "            194, 354, 810, 502, 388, 308, 536, 0, 342, 388, 730, 468, 354, 320,\n",
-    "            662, 742, 856\n",
-    "        ],\n",
-    "        [\n",
-    "            308, 696, 468, 844, 730, 194, 194, 342, 0, 274, 388, 810, 696, 662,\n",
-    "            320, 1084, 514\n",
-    "        ],\n",
-    "        [\n",
-    "            194, 742, 742, 890, 776, 240, 468, 388, 274, 0, 342, 536, 422, 388,\n",
-    "            274, 810, 468\n",
-    "        ],\n",
-    "        [\n",
-    "            536, 1084, 400, 1232, 1118, 582, 354, 730, 388, 342, 0, 878, 764,\n",
-    "            730, 388, 1152, 354\n",
-    "        ],\n",
-    "        [\n",
-    "            502, 594, 1278, 514, 400, 776, 1004, 468, 810, 536, 878, 0, 114,\n",
-    "            308, 650, 274, 844\n",
-    "        ],\n",
-    "        [\n",
-    "            388, 480, 1164, 628, 514, 662, 890, 354, 696, 422, 764, 114, 0, 194,\n",
-    "            536, 388, 730\n",
-    "        ],\n",
-    "        [\n",
-    "            354, 674, 1130, 822, 708, 628, 856, 320, 662, 388, 730, 308, 194, 0,\n",
-    "            342, 422, 536\n",
-    "        ],\n",
-    "        [\n",
-    "            468, 1016, 788, 1164, 1050, 514, 514, 662, 320, 274, 388, 650, 536,\n",
-    "            342, 0, 764, 194\n",
-    "        ],\n",
-    "        [\n",
-    "            776, 868, 1552, 560, 674, 1050, 1278, 742, 1084, 810, 1152, 274,\n",
-    "            388, 422, 764, 0, 798\n",
-    "        ],\n",
-    "        [\n",
-    "            662, 1210, 754, 1358, 1244, 708, 480, 856, 514, 468, 354, 844, 730,\n",
-    "            536, 194, 798, 0\n",
-    "        ],\n",
+    "    data[\"distance_matrix\"] = [\n",
+    "        # fmt: off\n",
+    "      [0, 548, 776, 696, 582, 274, 502, 194, 308, 194, 536, 502, 388, 354, 468, 776, 662],\n",
+    "      [548, 0, 684, 308, 194, 502, 730, 354, 696, 742, 1084, 594, 480, 674, 1016, 868, 1210],\n",
+    "      [776, 684, 0, 992, 878, 502, 274, 810, 468, 742, 400, 1278, 1164, 1130, 788, 1552, 754],\n",
+    "      [696, 308, 992, 0, 114, 650, 878, 502, 844, 890, 1232, 514, 628, 822, 1164, 560, 1358],\n",
+    "      [582, 194, 878, 114, 0, 536, 764, 388, 730, 776, 1118, 400, 514, 708, 1050, 674, 1244],\n",
+    "      [274, 502, 502, 650, 536, 0, 228, 308, 194, 240, 582, 776, 662, 628, 514, 1050, 708],\n",
+    "      [502, 730, 274, 878, 764, 228, 0, 536, 194, 468, 354, 1004, 890, 856, 514, 1278, 480],\n",
+    "      [194, 354, 810, 502, 388, 308, 536, 0, 342, 388, 730, 468, 354, 320, 662, 742, 856],\n",
+    "      [308, 696, 468, 844, 730, 194, 194, 342, 0, 274, 388, 810, 696, 662, 320, 1084, 514],\n",
+    "      [194, 742, 742, 890, 776, 240, 468, 388, 274, 0, 342, 536, 422, 388, 274, 810, 468],\n",
+    "      [536, 1084, 400, 1232, 1118, 582, 354, 730, 388, 342, 0, 878, 764, 730, 388, 1152, 354],\n",
+    "      [502, 594, 1278, 514, 400, 776, 1004, 468, 810, 536, 878, 0, 114, 308, 650, 274, 844],\n",
+    "      [388, 480, 1164, 628, 514, 662, 890, 354, 696, 422, 764, 114, 0, 194, 536, 388, 730],\n",
+    "      [354, 674, 1130, 822, 708, 628, 856, 320, 662, 388, 730, 308, 194, 0, 342, 422, 536],\n",
+    "      [468, 1016, 788, 1164, 1050, 514, 514, 662, 320, 274, 388, 650, 536, 342, 0, 764, 194],\n",
+    "      [776, 868, 1552, 560, 674, 1050, 1278, 742, 1084, 810, 1152, 274, 388, 422, 764, 0, 798],\n",
+    "      [662, 1210, 754, 1358, 1244, 708, 480, 856, 514, 468, 354, 844, 730, 536, 194, 798, 0],\n",
+    "        # fmt: on\n",
     "    ]\n",
-    "    data['num_vehicles'] = 4\n",
-    "    data['starts'] = [1, 2, 15, 16]\n",
-    "    data['ends'] = [0, 0, 0, 0]\n",
+    "    data[\"num_vehicles\"] = 4\n",
+    "    data[\"starts\"] = [1, 2, 15, 16]\n",
+    "    data[\"ends\"] = [0, 0, 0, 0]\n",
     "    return data\n",
     "\n",
     "\n",
     "def print_solution(data, manager, routing, solution):\n",
     "    \"\"\"Prints solution on console.\"\"\"\n",
-    "    print(f'Objective: {solution.ObjectiveValue()}')\n",
+    "    print(f\"Objective: {solution.ObjectiveValue()}\")\n",
     "    max_route_distance = 0\n",
-    "    for vehicle_id in range(data['num_vehicles']):\n",
+    "    for vehicle_id in range(data[\"num_vehicles\"]):\n",
     "        index = routing.Start(vehicle_id)\n",
-    "        plan_output = f'Route for vehicle {vehicle_id}:\\n'\n",
+    "        plan_output = f\"Route for vehicle {vehicle_id}:\\n\"\n",
     "        route_distance = 0\n",
     "        while not routing.IsEnd(index):\n",
-    "            plan_output += f' {manager.IndexToNode(index)} -> '\n",
+    "            plan_output += f\" {manager.IndexToNode(index)} -> \"\n",
     "            previous_index = index\n",
     "            index = solution.Value(routing.NextVar(index))\n",
     "            route_distance += routing.GetArcCostForVehicle(\n",
-    "                previous_index, index, vehicle_id)\n",
-    "        plan_output += f'{manager.IndexToNode(index)}\\n'\n",
-    "        plan_output += f'Distance of the route: {route_distance}m\\n'\n",
+    "                previous_index, index, vehicle_id\n",
+    "            )\n",
+    "        plan_output += f\"{manager.IndexToNode(index)}\\n\"\n",
+    "        plan_output += f\"Distance of the route: {route_distance}m\\n\"\n",
     "        print(plan_output)\n",
     "        max_route_distance = max(route_distance, max_route_distance)\n",
-    "    print(f'Maximum of the route distances: {max_route_distance}m')\n",
+    "    print(f\"Maximum of the route distances: {max_route_distance}m\")\n",
     "\n",
     "\n",
     "def main():\n",
@@ -193,21 +145,20 @@
     "    data = create_data_model()\n",
     "\n",
     "    # Create the routing index manager.\n",
-    "    manager = pywrapcp.RoutingIndexManager(len(data['distance_matrix']),\n",
-    "                                           data['num_vehicles'], data['starts'],\n",
-    "                                           data['ends'])\n",
+    "    manager = pywrapcp.RoutingIndexManager(\n",
+    "        len(data[\"distance_matrix\"]), data[\"num_vehicles\"], data[\"starts\"], data[\"ends\"]\n",
+    "    )\n",
     "\n",
     "    # Create Routing Model.\n",
     "    routing = pywrapcp.RoutingModel(manager)\n",
     "\n",
-    "\n",
     "    # Create and register a transit callback.\n",
     "    def distance_callback(from_index, to_index):\n",
     "        \"\"\"Returns the distance between the two nodes.\"\"\"\n",
     "        # Convert from routing variable Index to distance matrix NodeIndex.\n",
     "        from_node = manager.IndexToNode(from_index)\n",
     "        to_node = manager.IndexToNode(to_index)\n",
-    "        return data['distance_matrix'][from_node][to_node]\n",
+    "        return data[\"distance_matrix\"][from_node][to_node]\n",
     "\n",
     "    transit_callback_index = routing.RegisterTransitCallback(distance_callback)\n",
     "\n",
@@ -215,20 +166,22 @@
     "    routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)\n",
     "\n",
     "    # Add Distance constraint.\n",
-    "    dimension_name = 'Distance'\n",
+    "    dimension_name = \"Distance\"\n",
     "    routing.AddDimension(\n",
     "        transit_callback_index,\n",
     "        0,  # no slack\n",
     "        2000,  # vehicle maximum travel distance\n",
     "        True,  # start cumul to zero\n",
-    "        dimension_name)\n",
+    "        dimension_name,\n",
+    "    )\n",
     "    distance_dimension = routing.GetDimensionOrDie(dimension_name)\n",
     "    distance_dimension.SetGlobalSpanCostCoefficient(100)\n",
     "\n",
     "    # Setting first solution heuristic.\n",
     "    search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n",
     "    search_parameters.first_solution_strategy = (\n",
-    "        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)\n",
+    "        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC\n",
+    "    )\n",
     "\n",
     "    # Solve the problem.\n",
     "    solution = routing.SolveWithParameters(search_parameters)\n",
diff --git a/examples/notebook/constraint_solver/vrp_time_windows.ipynb b/examples/notebook/constraint_solver/vrp_time_windows.ipynb
index 988a89164f..65ce87405a 100644
--- a/examples/notebook/constraint_solver/vrp_time_windows.ipynb
+++ b/examples/notebook/constraint_solver/vrp_time_windows.ipynb
@@ -90,7 +90,7 @@
     "def create_data_model():\n",
     "    \"\"\"Stores the data for the problem.\"\"\"\n",
     "    data = {}\n",
-    "    data['time_matrix'] = [\n",
+    "    data[\"time_matrix\"] = [\n",
     "        [0, 6, 9, 8, 7, 3, 6, 2, 3, 2, 6, 6, 4, 4, 5, 9, 7],\n",
     "        [6, 0, 8, 3, 2, 6, 8, 4, 8, 8, 13, 7, 5, 8, 12, 10, 14],\n",
     "        [9, 8, 0, 11, 10, 6, 3, 9, 5, 8, 4, 15, 14, 13, 9, 18, 9],\n",
@@ -109,7 +109,7 @@
     "        [9, 10, 18, 6, 8, 12, 15, 8, 13, 9, 13, 3, 4, 5, 9, 0, 9],\n",
     "        [7, 14, 9, 16, 14, 8, 5, 10, 6, 5, 4, 10, 8, 6, 2, 9, 0],\n",
     "    ]\n",
-    "    data['time_windows'] = [\n",
+    "    data[\"time_windows\"] = [\n",
     "        (0, 5),  # depot\n",
     "        (7, 12),  # 1\n",
     "        (10, 15),  # 2\n",
@@ -128,34 +128,36 @@
     "        (10, 15),  # 15\n",
     "        (11, 15),  # 16\n",
     "    ]\n",
-    "    data['num_vehicles'] = 4\n",
-    "    data['depot'] = 0\n",
+    "    data[\"num_vehicles\"] = 4\n",
+    "    data[\"depot\"] = 0\n",
     "    return data\n",
     "\n",
     "\n",
     "def print_solution(data, manager, routing, solution):\n",
     "    \"\"\"Prints solution on console.\"\"\"\n",
-    "    print(f'Objective: {solution.ObjectiveValue()}')\n",
-    "    time_dimension = routing.GetDimensionOrDie('Time')\n",
+    "    print(f\"Objective: {solution.ObjectiveValue()}\")\n",
+    "    time_dimension = routing.GetDimensionOrDie(\"Time\")\n",
     "    total_time = 0\n",
-    "    for vehicle_id in range(data['num_vehicles']):\n",
+    "    for vehicle_id in range(data[\"num_vehicles\"]):\n",
     "        index = routing.Start(vehicle_id)\n",
-    "        plan_output = f'Route for vehicle {vehicle_id}:\\n'\n",
+    "        plan_output = f\"Route for vehicle {vehicle_id}:\\n\"\n",
     "        while not routing.IsEnd(index):\n",
     "            time_var = time_dimension.CumulVar(index)\n",
     "            plan_output += (\n",
-    "                f'{manager.IndexToNode(index)}'\n",
-    "                f' Time({solution.Min(time_var)},{solution.Max(time_var)})'\n",
-    "                ' -> ')\n",
+    "                f\"{manager.IndexToNode(index)}\"\n",
+    "                f\" Time({solution.Min(time_var)},{solution.Max(time_var)})\"\n",
+    "                \" -> \"\n",
+    "            )\n",
     "            index = solution.Value(routing.NextVar(index))\n",
     "        time_var = time_dimension.CumulVar(index)\n",
     "        plan_output += (\n",
-    "            f'{manager.IndexToNode(index)}'\n",
-    "            f' Time({solution.Min(time_var)},{solution.Max(time_var)})\\n')\n",
-    "        plan_output += f'Time of the route: {solution.Min(time_var)}min\\n'\n",
+    "            f\"{manager.IndexToNode(index)}\"\n",
+    "            f\" Time({solution.Min(time_var)},{solution.Max(time_var)})\\n\"\n",
+    "        )\n",
+    "        plan_output += f\"Time of the route: {solution.Min(time_var)}min\\n\"\n",
     "        print(plan_output)\n",
     "        total_time += solution.Min(time_var)\n",
-    "    print(f'Total time of all routes: {total_time}min')\n",
+    "    print(f\"Total time of all routes: {total_time}min\")\n",
     "\n",
     "\n",
     "def main():\n",
@@ -164,20 +166,20 @@
     "    data = create_data_model()\n",
     "\n",
     "    # Create the routing index manager.\n",
-    "    manager = pywrapcp.RoutingIndexManager(len(data['time_matrix']),\n",
-    "                                           data['num_vehicles'], data['depot'])\n",
+    "    manager = pywrapcp.RoutingIndexManager(\n",
+    "        len(data[\"time_matrix\"]), data[\"num_vehicles\"], data[\"depot\"]\n",
+    "    )\n",
     "\n",
     "    # Create Routing Model.\n",
     "    routing = pywrapcp.RoutingModel(manager)\n",
     "\n",
-    "\n",
     "    # Create and register a transit callback.\n",
     "    def time_callback(from_index, to_index):\n",
     "        \"\"\"Returns the travel time between the two nodes.\"\"\"\n",
     "        # Convert from routing variable Index to time matrix NodeIndex.\n",
     "        from_node = manager.IndexToNode(from_index)\n",
     "        to_node = manager.IndexToNode(to_index)\n",
-    "        return data['time_matrix'][from_node][to_node]\n",
+    "        return data[\"time_matrix\"][from_node][to_node]\n",
     "\n",
     "    transit_callback_index = routing.RegisterTransitCallback(time_callback)\n",
     "\n",
@@ -185,39 +187,41 @@
     "    routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)\n",
     "\n",
     "    # Add Time Windows constraint.\n",
-    "    time = 'Time'\n",
+    "    time = \"Time\"\n",
     "    routing.AddDimension(\n",
     "        transit_callback_index,\n",
     "        30,  # allow waiting time\n",
     "        30,  # maximum time per vehicle\n",
     "        False,  # Don't force start cumul to zero.\n",
-    "        time)\n",
+    "        time,\n",
+    "    )\n",
     "    time_dimension = routing.GetDimensionOrDie(time)\n",
     "    # Add time window constraints for each location except depot.\n",
-    "    for location_idx, time_window in enumerate(data['time_windows']):\n",
-    "        if location_idx == data['depot']:\n",
+    "    for location_idx, time_window in enumerate(data[\"time_windows\"]):\n",
+    "        if location_idx == data[\"depot\"]:\n",
     "            continue\n",
     "        index = manager.NodeToIndex(location_idx)\n",
     "        time_dimension.CumulVar(index).SetRange(time_window[0], time_window[1])\n",
     "    # Add time window constraints for each vehicle start node.\n",
-    "    depot_idx = data['depot']\n",
-    "    for vehicle_id in range(data['num_vehicles']):\n",
+    "    depot_idx = data[\"depot\"]\n",
+    "    for vehicle_id in range(data[\"num_vehicles\"]):\n",
     "        index = routing.Start(vehicle_id)\n",
     "        time_dimension.CumulVar(index).SetRange(\n",
-    "            data['time_windows'][depot_idx][0],\n",
-    "            data['time_windows'][depot_idx][1])\n",
+    "            data[\"time_windows\"][depot_idx][0], data[\"time_windows\"][depot_idx][1]\n",
+    "        )\n",
     "\n",
     "    # Instantiate route start and end times to produce feasible times.\n",
-    "    for i in range(data['num_vehicles']):\n",
+    "    for i in range(data[\"num_vehicles\"]):\n",
     "        routing.AddVariableMinimizedByFinalizer(\n",
-    "            time_dimension.CumulVar(routing.Start(i)))\n",
-    "        routing.AddVariableMinimizedByFinalizer(\n",
-    "            time_dimension.CumulVar(routing.End(i)))\n",
+    "            time_dimension.CumulVar(routing.Start(i))\n",
+    "        )\n",
+    "        routing.AddVariableMinimizedByFinalizer(time_dimension.CumulVar(routing.End(i)))\n",
     "\n",
     "    # Setting first solution heuristic.\n",
     "    search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n",
     "    search_parameters.first_solution_strategy = (\n",
-    "        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)\n",
+    "        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC\n",
+    "    )\n",
     "\n",
     "    # Solve the problem.\n",
     "    solution = routing.SolveWithParameters(search_parameters)\n",
diff --git a/examples/notebook/constraint_solver/vrp_tokens.ipynb b/examples/notebook/constraint_solver/vrp_tokens.ipynb
index 5bac71fd17..4097c1c96c 100644
--- a/examples/notebook/constraint_solver/vrp_tokens.ipynb
+++ b/examples/notebook/constraint_solver/vrp_tokens.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Simple VRP with special locations which need to be visited at end of the route.\n"
    ]
   },
@@ -90,7 +91,7 @@
     "    \"\"\"Stores the data for the problem.\"\"\"\n",
     "    data = {}\n",
     "    # Special location don't consume token, while regular one consume one\n",
-    "    data['tokens'] = [\n",
+    "    data[\"tokens\"] = [\n",
     "        0,  # 0 depot\n",
     "        0,  # 1 special node\n",
     "        0,  # 2 special node\n",
@@ -112,20 +113,20 @@
     "        -1,  # 18\n",
     "    ]\n",
     "    # just need to be big enough, not a limiting factor\n",
-    "    data['vehicle_tokens'] = [20, 20, 20, 20]\n",
-    "    data['num_vehicles'] = 4\n",
-    "    data['depot'] = 0\n",
+    "    data[\"vehicle_tokens\"] = [20, 20, 20, 20]\n",
+    "    data[\"num_vehicles\"] = 4\n",
+    "    data[\"depot\"] = 0\n",
     "    return data\n",
     "\n",
     "\n",
     "def print_solution(manager, routing, solution):\n",
     "    \"\"\"Prints solution on console.\"\"\"\n",
-    "    print(f'Objective: {solution.ObjectiveValue()}')\n",
-    "    token_dimension = routing.GetDimensionOrDie('Token')\n",
+    "    print(f\"Objective: {solution.ObjectiveValue()}\")\n",
+    "    token_dimension = routing.GetDimensionOrDie(\"Token\")\n",
     "    total_distance = 0\n",
     "    total_token = 0\n",
     "    for vehicle_id in range(manager.GetNumberOfVehicles()):\n",
-    "        plan_output = f'Route for vehicle {vehicle_id}:\\n'\n",
+    "        plan_output = f\"Route for vehicle {vehicle_id}:\\n\"\n",
     "        index = routing.Start(vehicle_id)\n",
     "        total_token += solution.Value(token_dimension.CumulVar(index))\n",
     "        route_distance = 0\n",
@@ -134,20 +135,21 @@
     "            node_index = manager.IndexToNode(index)\n",
     "            token_var = token_dimension.CumulVar(index)\n",
     "            route_token = solution.Value(token_var)\n",
-    "            plan_output += f' {node_index} Token({route_token}) -> '\n",
+    "            plan_output += f\" {node_index} Token({route_token}) -> \"\n",
     "            previous_index = index\n",
     "            index = solution.Value(routing.NextVar(index))\n",
     "            route_distance += routing.GetArcCostForVehicle(\n",
-    "                previous_index, index, vehicle_id)\n",
+    "                previous_index, index, vehicle_id\n",
+    "            )\n",
     "        node_index = manager.IndexToNode(index)\n",
     "        token_var = token_dimension.CumulVar(index)\n",
     "        route_token = solution.Value(token_var)\n",
-    "        plan_output += f' {node_index} Token({route_token})\\n'\n",
-    "        plan_output += f'Distance of the route: {route_distance}m\\n'\n",
+    "        plan_output += f\" {node_index} Token({route_token})\\n\"\n",
+    "        plan_output += f\"Distance of the route: {route_distance}m\\n\"\n",
     "        total_distance += route_distance\n",
     "        print(plan_output)\n",
-    "    print(f'Total distance of all routes: {total_distance}m')\n",
-    "    print(f'Total token of all routes: {total_token}')\n",
+    "    print(f\"Total distance of all routes: {total_distance}m\")\n",
+    "    print(f\"Total token of all routes: {total_token}\")\n",
     "\n",
     "\n",
     "def main():\n",
@@ -156,8 +158,9 @@
     "    data = create_data_model()\n",
     "\n",
     "    # Create the routing index manager.\n",
-    "    manager = pywrapcp.RoutingIndexManager(len(data['tokens']),\n",
-    "                                           data['num_vehicles'], data['depot'])\n",
+    "    manager = pywrapcp.RoutingIndexManager(\n",
+    "        len(data[\"tokens\"]), data[\"num_vehicles\"], data[\"depot\"]\n",
+    "    )\n",
     "\n",
     "    # Create Routing Model.\n",
     "    routing = pywrapcp.RoutingModel(manager)\n",
@@ -176,8 +179,9 @@
     "        0,  # null slack\n",
     "        3000,  # maximum distance per vehicle\n",
     "        True,  # start cumul to zero\n",
-    "        'distance')\n",
-    "    distance_dimension = routing.GetDimensionOrDie('distance')\n",
+    "        \"distance\",\n",
+    "    )\n",
+    "    distance_dimension = routing.GetDimensionOrDie(\"distance\")\n",
     "    distance_dimension.SetGlobalSpanCostCoefficient(100)\n",
     "\n",
     "    # Define cost of each arc.\n",
@@ -188,17 +192,18 @@
     "        \"\"\"Returns the number of token consumed by the node.\"\"\"\n",
     "        # Convert from routing variable Index to tokens NodeIndex.\n",
     "        from_node = manager.IndexToNode(from_index)\n",
-    "        return data['tokens'][from_node]\n",
+    "        return data[\"tokens\"][from_node]\n",
     "\n",
     "    token_callback_index = routing.RegisterUnaryTransitCallback(token_callback)\n",
     "    routing.AddDimensionWithVehicleCapacity(\n",
     "        token_callback_index,\n",
     "        0,  # null capacity slack\n",
-    "        data['vehicle_tokens'],  # vehicle maximum tokens\n",
+    "        data[\"vehicle_tokens\"],  # vehicle maximum tokens\n",
     "        False,  # start cumul to zero\n",
-    "        'Token')\n",
+    "        \"Token\",\n",
+    "    )\n",
     "    # Add constraint: special node can only be visited if token remaining is zero\n",
-    "    token_dimension = routing.GetDimensionOrDie('Token')\n",
+    "    token_dimension = routing.GetDimensionOrDie(\"Token\")\n",
     "    for node in range(1, 6):\n",
     "        index = manager.NodeToIndex(node)\n",
     "        routing.solver().Add(token_dimension.CumulVar(index) == 0)\n",
@@ -206,16 +211,20 @@
     "    # Instantiate route start and end times to produce feasible times.\n",
     "    for i in range(manager.GetNumberOfVehicles()):\n",
     "        routing.AddVariableMinimizedByFinalizer(\n",
-    "            token_dimension.CumulVar(routing.Start(i)))\n",
+    "            token_dimension.CumulVar(routing.Start(i))\n",
+    "        )\n",
     "        routing.AddVariableMinimizedByFinalizer(\n",
-    "            token_dimension.CumulVar(routing.End(i)))\n",
+    "            token_dimension.CumulVar(routing.End(i))\n",
+    "        )\n",
     "\n",
     "    # Setting first solution heuristic.\n",
     "    search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n",
     "    search_parameters.first_solution_strategy = (\n",
-    "        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)\n",
+    "        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC\n",
+    "    )\n",
     "    search_parameters.local_search_metaheuristic = (\n",
-    "        routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH)\n",
+    "        routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH\n",
+    "    )\n",
     "    search_parameters.time_limit.FromSeconds(1)\n",
     "\n",
     "    # Solve the problem.\n",
@@ -225,7 +234,7 @@
     "    if solution:\n",
     "        print_solution(manager, routing, solution)\n",
     "    else:\n",
-    "        print('No solution found !')\n",
+    "        print(\"No solution found !\")\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/constraint_solver/vrp_with_time_limit.ipynb b/examples/notebook/constraint_solver/vrp_with_time_limit.ipynb
index f6d30024f1..be2f359492 100644
--- a/examples/notebook/constraint_solver/vrp_with_time_limit.ipynb
+++ b/examples/notebook/constraint_solver/vrp_with_time_limit.ipynb
@@ -89,23 +89,24 @@
     "\n",
     "def print_solution(manager, routing, solution):\n",
     "    \"\"\"Prints solution on console.\"\"\"\n",
-    "    print(f'Objective: {solution.ObjectiveValue()}')\n",
+    "    print(f\"Objective: {solution.ObjectiveValue()}\")\n",
     "    max_route_distance = 0\n",
     "    for vehicle_id in range(manager.GetNumberOfVehicles()):\n",
     "        index = routing.Start(vehicle_id)\n",
-    "        plan_output = f'Route for vehicle {vehicle_id}:\\n'\n",
+    "        plan_output = f\"Route for vehicle {vehicle_id}:\\n\"\n",
     "        route_distance = 0\n",
     "        while not routing.IsEnd(index):\n",
-    "            plan_output += f' {manager.IndexToNode(index)} -> '\n",
+    "            plan_output += f\" {manager.IndexToNode(index)} -> \"\n",
     "            previous_index = index\n",
     "            index = solution.Value(routing.NextVar(index))\n",
     "            route_distance += routing.GetArcCostForVehicle(\n",
-    "                previous_index, index, vehicle_id)\n",
-    "        plan_output += f'{manager.IndexToNode(index)}\\n'\n",
-    "        plan_output += f'Distance of the route: {route_distance}m\\n'\n",
+    "                previous_index, index, vehicle_id\n",
+    "            )\n",
+    "        plan_output += f\"{manager.IndexToNode(index)}\\n\"\n",
+    "        plan_output += f\"Distance of the route: {route_distance}m\\n\"\n",
     "        print(plan_output)\n",
     "        max_route_distance = max(route_distance, max_route_distance)\n",
-    "    print(f'Maximum of the route distances: {max_route_distance}m')\n",
+    "    print(f\"Maximum of the route distances: {max_route_distance}m\")\n",
     "\n",
     "\n",
     "def main():\n",
@@ -134,22 +135,25 @@
     "    routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)\n",
     "\n",
     "    # Add Distance constraint.\n",
-    "    dimension_name = 'Distance'\n",
+    "    dimension_name = \"Distance\"\n",
     "    routing.AddDimension(\n",
     "        transit_callback_index,\n",
     "        0,  # no slack\n",
     "        3000,  # vehicle maximum travel distance\n",
     "        True,  # start cumul to zero\n",
-    "        dimension_name)\n",
+    "        dimension_name,\n",
+    "    )\n",
     "    distance_dimension = routing.GetDimensionOrDie(dimension_name)\n",
     "    distance_dimension.SetGlobalSpanCostCoefficient(100)\n",
     "\n",
     "    # Setting first solution heuristic.\n",
     "    search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n",
     "    search_parameters.first_solution_strategy = (\n",
-    "        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)\n",
+    "        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC\n",
+    "    )\n",
     "    search_parameters.local_search_metaheuristic = (\n",
-    "        routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH)\n",
+    "        routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH\n",
+    "    )\n",
     "    search_parameters.log_search = True\n",
     "    search_parameters.time_limit.FromSeconds(5)\n",
     "\n",
diff --git a/examples/notebook/constraint_solver/vrpgs.ipynb b/examples/notebook/constraint_solver/vrpgs.ipynb
index 4fc88189a2..d6a76a0f17 100644
--- a/examples/notebook/constraint_solver/vrpgs.ipynb
+++ b/examples/notebook/constraint_solver/vrpgs.ipynb
@@ -101,55 +101,50 @@
     "    data = {}\n",
     "    # Locations in block unit\n",
     "    locations_ = [\n",
-    "        (4, 4),  # depot\n",
-    "        (2, 0),\n",
-    "        (8, 0),  # locations to visit\n",
-    "        (0, 1),\n",
-    "        (1, 1),\n",
-    "        (5, 2),\n",
-    "        (7, 2),\n",
-    "        (3, 3),\n",
-    "        (6, 3),\n",
-    "        (5, 5),\n",
-    "        (8, 5),\n",
-    "        (1, 6),\n",
-    "        (2, 6),\n",
-    "        (3, 7),\n",
-    "        (6, 7),\n",
-    "        (0, 8),\n",
-    "        (7, 8),\n",
+    "        # fmt: off\n",
+    "      (4, 4),  # depot\n",
+    "      (2, 0), (8, 0),  # locations to visit\n",
+    "      (0, 1), (1, 1),\n",
+    "      (5, 2), (7, 2),\n",
+    "      (3, 3), (6, 3),\n",
+    "      (5, 5), (8, 5),\n",
+    "      (1, 6), (2, 6),\n",
+    "      (3, 7), (6, 7),\n",
+    "      (0, 8), (7, 8),\n",
+    "        # fmt: on\n",
     "    ]\n",
     "    # Compute locations in meters using the block dimension defined as follow\n",
     "    # Manhattan average block: 750ft x 264ft -> 228m x 80m\n",
     "    # here we use: 114m x 80m city block\n",
     "    # src: https://nyti.ms/2GDoRIe 'NY Times: Know Your distance'\n",
-    "    data['locations'] = [(l[0] * 114, l[1] * 80) for l in locations_]\n",
-    "    data['num_locations'] = len(data['locations'])\n",
-    "    data['num_vehicles'] = 4\n",
-    "    data['depot'] = 0\n",
+    "    data[\"locations\"] = [(l[0] * 114, l[1] * 80) for l in locations_]\n",
+    "    data[\"num_locations\"] = len(data[\"locations\"])\n",
+    "    data[\"num_vehicles\"] = 4\n",
+    "    data[\"depot\"] = 0\n",
     "    return data\n",
     "\n",
     "\n",
     "\n",
     "def print_solution(data, manager, routing, assignment):\n",
     "    \"\"\"Prints solution on console.\"\"\"\n",
-    "    print(f'Objective: {assignment.ObjectiveValue()}')\n",
+    "    print(f\"Objective: {assignment.ObjectiveValue()}\")\n",
     "    total_distance = 0\n",
-    "    for vehicle_id in range(data['num_vehicles']):\n",
+    "    for vehicle_id in range(data[\"num_vehicles\"]):\n",
     "        index = routing.Start(vehicle_id)\n",
-    "        plan_output = f'Route for vehicle {vehicle_id}:\\n'\n",
+    "        plan_output = f\"Route for vehicle {vehicle_id}:\\n\"\n",
     "        route_distance = 0\n",
     "        while not routing.IsEnd(index):\n",
-    "            plan_output += f' {manager.IndexToNode(index)} ->'\n",
+    "            plan_output += f\" {manager.IndexToNode(index)} ->\"\n",
     "            previous_index = index\n",
     "            index = assignment.Value(routing.NextVar(index))\n",
     "            route_distance += routing.GetArcCostForVehicle(\n",
-    "                previous_index, index, vehicle_id)\n",
-    "        plan_output += f' {manager.IndexToNode(index)}\\n'\n",
-    "        plan_output += f'Distance of the route: {route_distance}m\\n'\n",
+    "                previous_index, index, vehicle_id\n",
+    "            )\n",
+    "        plan_output += f\" {manager.IndexToNode(index)}\\n\"\n",
+    "        plan_output += f\"Distance of the route: {route_distance}m\\n\"\n",
     "        print(plan_output)\n",
     "        total_distance += route_distance\n",
-    "    print(f'Total Distance of all routes: {total_distance}m')\n",
+    "    print(f\"Total Distance of all routes: {total_distance}m\")\n",
     "\n",
     "\n",
     "\n",
@@ -158,22 +153,22 @@
     "#######################\n",
     "def manhattan_distance(position_1, position_2):\n",
     "    \"\"\"Computes the Manhattan distance between two points.\"\"\"\n",
-    "    return (abs(position_1[0] - position_2[0]) +\n",
-    "            abs(position_1[1] - position_2[1]))\n",
+    "    return abs(position_1[0] - position_2[0]) + abs(position_1[1] - position_2[1])\n",
     "\n",
     "\n",
     "def create_distance_evaluator(data):\n",
     "    \"\"\"Creates callback to return distance between points.\"\"\"\n",
     "    distances_ = {}\n",
     "    # precompute distance between location to have distance callback in O(1)\n",
-    "    for from_node in range(data['num_locations']):\n",
+    "    for from_node in range(data[\"num_locations\"]):\n",
     "        distances_[from_node] = {}\n",
-    "        for to_node in range(data['num_locations']):\n",
+    "        for to_node in range(data[\"num_locations\"]):\n",
     "            if from_node == to_node:\n",
     "                distances_[from_node][to_node] = 0\n",
     "            else:\n",
-    "                distances_[from_node][to_node] = (manhattan_distance(\n",
-    "                    data['locations'][from_node], data['locations'][to_node]))\n",
+    "                distances_[from_node][to_node] = manhattan_distance(\n",
+    "                    data[\"locations\"][from_node], data[\"locations\"][to_node]\n",
+    "                )\n",
     "\n",
     "    def distance_evaluator(manager, from_index, to_index):\n",
     "        \"\"\"Returns the manhattan distance between the two nodes.\"\"\"\n",
@@ -187,13 +182,14 @@
     "\n",
     "def add_distance_dimension(routing, distance_evaluator_index):\n",
     "    \"\"\"Add Global Span constraint.\"\"\"\n",
-    "    distance = 'Distance'\n",
+    "    distance = \"Distance\"\n",
     "    routing.AddDimension(\n",
     "        distance_evaluator_index,\n",
     "        0,  # null slack\n",
     "        3000,  # maximum distance per vehicle\n",
     "        True,  # start cumul to zero\n",
-    "        distance)\n",
+    "        distance,\n",
+    "    )\n",
     "    distance_dimension = routing.GetDimensionOrDie(distance)\n",
     "    # Try to minimize the max distance among vehicles.\n",
     "    # /!\\ It doesn't mean the standard deviation is minimized\n",
@@ -206,15 +202,17 @@
     "    data = create_data_model()\n",
     "\n",
     "    # Create the routing index manager.\n",
-    "    manager = pywrapcp.RoutingIndexManager(data['num_locations'],\n",
-    "                                           data['num_vehicles'], data['depot'])\n",
+    "    manager = pywrapcp.RoutingIndexManager(\n",
+    "        data[\"num_locations\"], data[\"num_vehicles\"], data[\"depot\"]\n",
+    "    )\n",
     "\n",
     "    # Create Routing Model.\n",
     "    routing = pywrapcp.RoutingModel(manager)\n",
     "\n",
     "    # Define weight of each edge\n",
     "    distance_evaluator_index = routing.RegisterTransitCallback(\n",
-    "        functools.partial(create_distance_evaluator(data), manager))\n",
+    "        functools.partial(create_distance_evaluator(data), manager)\n",
+    "    )\n",
     "\n",
     "    # Define cost of each arc.\n",
     "    routing.SetArcCostEvaluatorOfAllVehicles(distance_evaluator_index)\n",
@@ -225,7 +223,8 @@
     "    # Setting first solution heuristic.\n",
     "    search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n",
     "    search_parameters.first_solution_strategy = (\n",
-    "        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)\n",
+    "        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC\n",
+    "    )\n",
     "\n",
     "    # Solve the problem.\n",
     "    solution = routing.SolveWithParameters(search_parameters)\n",
@@ -234,7 +233,7 @@
     "    if solution:\n",
     "        print_solution(data, manager, routing, solution)\n",
     "    else:\n",
-    "        print('No solution found !')\n",
+    "        print(\"No solution found !\")\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/constraint_solver/vrptw_store_solution_data.ipynb b/examples/notebook/constraint_solver/vrptw_store_solution_data.ipynb
index 88ba7d846e..08dd65d82c 100644
--- a/examples/notebook/constraint_solver/vrptw_store_solution_data.ipynb
+++ b/examples/notebook/constraint_solver/vrptw_store_solution_data.ipynb
@@ -90,7 +90,7 @@
     "def create_data_model():\n",
     "    \"\"\"Stores the data for the problem.\"\"\"\n",
     "    data = {}\n",
-    "    data['time_matrix'] = [\n",
+    "    data[\"time_matrix\"] = [\n",
     "        [0, 6, 9, 8, 7, 3, 6, 2, 3, 2, 6, 6, 4, 4, 5, 9, 7],\n",
     "        [6, 0, 8, 3, 2, 6, 8, 4, 8, 8, 13, 7, 5, 8, 12, 10, 14],\n",
     "        [9, 8, 0, 11, 10, 6, 3, 9, 5, 8, 4, 15, 14, 13, 9, 18, 9],\n",
@@ -109,7 +109,7 @@
     "        [9, 10, 18, 6, 8, 12, 15, 8, 13, 9, 13, 3, 4, 5, 9, 0, 9],\n",
     "        [7, 14, 9, 16, 14, 8, 5, 10, 6, 5, 4, 10, 8, 6, 2, 9, 0],\n",
     "    ]\n",
-    "    data['time_windows'] = [\n",
+    "    data[\"time_windows\"] = [\n",
     "        (0, 5),  # depot\n",
     "        (7, 12),  # 1\n",
     "        (10, 15),  # 2\n",
@@ -128,8 +128,8 @@
     "        (10, 15),  # 15\n",
     "        (11, 15),  # 16\n",
     "    ]\n",
-    "    data['num_vehicles'] = 4\n",
-    "    data['depot'] = 0\n",
+    "    data[\"num_vehicles\"] = 4\n",
+    "    data[\"depot\"] = 0\n",
     "    return data\n",
     "\n",
     "\n",
@@ -137,21 +137,35 @@
     "def print_solution(routes, cumul_data):\n",
     "    \"\"\"Print the solution.\"\"\"\n",
     "    total_time = 0\n",
-    "    route_str = ''\n",
+    "    route_str = \"\"\n",
     "    for i, route in enumerate(routes):\n",
-    "        route_str += 'Route ' + str(i) + ':\\n'\n",
+    "        route_str += \"Route \" + str(i) + \":\\n\"\n",
     "        start_time = cumul_data[i][0][0]\n",
     "        end_time = cumul_data[i][0][1]\n",
-    "        route_str += '  ' + str(route[0]) + \\\n",
-    "                     ' Time(' + str(start_time) + ', ' + str(end_time) + ')'\n",
+    "        route_str += (\n",
+    "            \"  \"\n",
+    "            + str(route[0])\n",
+    "            + \" Time(\"\n",
+    "            + str(start_time)\n",
+    "            + \", \"\n",
+    "            + str(end_time)\n",
+    "            + \")\"\n",
+    "        )\n",
     "        for j in range(1, len(route)):\n",
     "            start_time = cumul_data[i][j][0]\n",
     "            end_time = cumul_data[i][j][1]\n",
-    "            route_str += ' -> ' + str(route[j]) + \\\n",
-    "                         ' Time(' + str(start_time) + ', ' + str(end_time) + ')'\n",
-    "        route_str += f'\\n  Route time: {start_time}min\\n\\n'\n",
+    "            route_str += (\n",
+    "                \" -> \"\n",
+    "                + str(route[j])\n",
+    "                + \" Time(\"\n",
+    "                + str(start_time)\n",
+    "                + \", \"\n",
+    "                + str(end_time)\n",
+    "                + \")\"\n",
+    "            )\n",
+    "        route_str += f\"\\n  Route time: {start_time}min\\n\\n\"\n",
     "        total_time += cumul_data[i][len(route) - 1][0]\n",
-    "    route_str += f'Total time: {total_time}min'\n",
+    "    route_str += f\"Total time: {total_time}min\"\n",
     "    print(route_str)\n",
     "\n",
     "\n",
@@ -200,20 +214,20 @@
     "    data = create_data_model()\n",
     "\n",
     "    # Create the routing index manager.\n",
-    "    manager = pywrapcp.RoutingIndexManager(len(data['time_matrix']),\n",
-    "                                           data['num_vehicles'], data['depot'])\n",
+    "    manager = pywrapcp.RoutingIndexManager(\n",
+    "        len(data[\"time_matrix\"]), data[\"num_vehicles\"], data[\"depot\"]\n",
+    "    )\n",
     "\n",
     "    # Create Routing Model.\n",
     "    routing = pywrapcp.RoutingModel(manager)\n",
     "\n",
-    "\n",
     "    # Create and register a transit callback.\n",
     "    def time_callback(from_index, to_index):\n",
     "        \"\"\"Returns the travel time between the two nodes.\"\"\"\n",
     "        # Convert from routing variable Index to time matrix NodeIndex.\n",
     "        from_node = manager.IndexToNode(from_index)\n",
     "        to_node = manager.IndexToNode(to_index)\n",
-    "        return data['time_matrix'][from_node][to_node]\n",
+    "        return data[\"time_matrix\"][from_node][to_node]\n",
     "\n",
     "    transit_callback_index = routing.RegisterTransitCallback(time_callback)\n",
     "\n",
@@ -221,38 +235,41 @@
     "    routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)\n",
     "\n",
     "    # Add Time Windows constraint.\n",
-    "    time = 'Time'\n",
+    "    time = \"Time\"\n",
     "\n",
     "    routing.AddDimension(\n",
     "        transit_callback_index,\n",
     "        30,  # allow waiting time\n",
     "        30,  # maximum time per vehicle\n",
     "        False,  # Don't force cumulative time to be 0 at start of routes.\n",
-    "        time)\n",
+    "        time,\n",
+    "    )\n",
     "    time_dimension = routing.GetDimensionOrDie(time)\n",
     "    # Add time window constraints for each location except depot.\n",
-    "    for location_idx, time_window in enumerate(data['time_windows']):\n",
+    "    for location_idx, time_window in enumerate(data[\"time_windows\"]):\n",
     "        if location_idx == 0:\n",
     "            continue\n",
     "        index = manager.NodeToIndex(location_idx)\n",
     "        time_dimension.CumulVar(index).SetRange(time_window[0], time_window[1])\n",
     "    # Add time window constraints for each vehicle start node.\n",
-    "    for vehicle_id in range(data['num_vehicles']):\n",
+    "    for vehicle_id in range(data[\"num_vehicles\"]):\n",
     "        index = routing.Start(vehicle_id)\n",
-    "        time_dimension.CumulVar(index).SetRange(data['time_windows'][0][0],\n",
-    "                                                data['time_windows'][0][1])\n",
+    "        time_dimension.CumulVar(index).SetRange(\n",
+    "            data[\"time_windows\"][0][0], data[\"time_windows\"][0][1]\n",
+    "        )\n",
     "\n",
     "    # Instantiate route start and end times to produce feasible times.\n",
-    "    for i in range(data['num_vehicles']):\n",
+    "    for i in range(data[\"num_vehicles\"]):\n",
     "        routing.AddVariableMinimizedByFinalizer(\n",
-    "            time_dimension.CumulVar(routing.Start(i)))\n",
-    "        routing.AddVariableMinimizedByFinalizer(\n",
-    "            time_dimension.CumulVar(routing.End(i)))\n",
+    "            time_dimension.CumulVar(routing.Start(i))\n",
+    "        )\n",
+    "        routing.AddVariableMinimizedByFinalizer(time_dimension.CumulVar(routing.End(i)))\n",
     "\n",
     "    # Setting first solution heuristic.\n",
     "    search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n",
     "    search_parameters.first_solution_strategy = (\n",
-    "        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)\n",
+    "        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC\n",
+    "    )\n",
     "\n",
     "    # Solve the problem.\n",
     "    solution = routing.SolveWithParameters(search_parameters)\n",
diff --git a/examples/notebook/examples/appointments.ipynb b/examples/notebook/examples/appointments.ipynb
index 20cb6a090c..aa5bda9a11 100644
--- a/examples/notebook/examples/appointments.ipynb
+++ b/examples/notebook/examples/appointments.ipynb
@@ -92,12 +92,11 @@
     "from ortools.linear_solver import pywraplp\n",
     "from ortools.sat.python import cp_model\n",
     "\n",
-    "_LOAD_MIN = flags.DEFINE_integer('load_min', 480, 'Minimum load in minutes.')\n",
-    "_LOAD_MAX = flags.DEFINE_integer('load_max', 540, 'Maximum load in minutes.')\n",
-    "_COMMUTE_TIME = flags.DEFINE_integer('commute_time', 30,\n",
-    "                                     'Commute time in minutes.')\n",
-    "_NUM_WORKERS = flags.DEFINE_integer('num_workers', 98,\n",
-    "                                    'Maximum number of workers.')\n",
+    "\n",
+    "_LOAD_MIN = flags.DEFINE_integer(\"load_min\", 480, \"Minimum load in minutes.\")\n",
+    "_LOAD_MAX = flags.DEFINE_integer(\"load_max\", 540, \"Maximum load in minutes.\")\n",
+    "_COMMUTE_TIME = flags.DEFINE_integer(\"commute_time\", 30, \"Commute time in minutes.\")\n",
+    "_NUM_WORKERS = flags.DEFINE_integer(\"num_workers\", 98, \"Maximum number of workers.\")\n",
     "\n",
     "\n",
     "class AllSolutionCollector(cp_model.CpSolverSolutionCallback):\n",
@@ -118,24 +117,21 @@
     "        return self.__collect\n",
     "\n",
     "\n",
-    "def EnumerateAllKnapsacksWithRepetition(item_sizes, total_size_min,\n",
-    "                                        total_size_max):\n",
+    "def EnumerateAllKnapsacksWithRepetition(item_sizes, total_size_min, total_size_max):\n",
     "    \"\"\"Enumerate all possible knapsacks with total size in the given range.\n",
     "\n",
-    "  Args:\n",
-    "    item_sizes: a list of integers. item_sizes[i] is the size of item #i.\n",
-    "    total_size_min: an integer, the minimum total size.\n",
-    "    total_size_max: an integer, the maximum total size.\n",
+    "    Args:\n",
+    "      item_sizes: a list of integers. item_sizes[i] is the size of item #i.\n",
+    "      total_size_min: an integer, the minimum total size.\n",
+    "      total_size_max: an integer, the maximum total size.\n",
     "\n",
-    "  Returns:\n",
-    "    The list of all the knapsacks whose total size is in the given inclusive\n",
-    "    range. Each knapsack is a list [#item0, #item1, ... ], where #itemK is an\n",
-    "    nonnegative integer: the number of times we put item #K in the knapsack.\n",
-    "  \"\"\"\n",
+    "    Returns:\n",
+    "      The list of all the knapsacks whose total size is in the given inclusive\n",
+    "      range. Each knapsack is a list [#item0, #item1, ... ], where #itemK is an\n",
+    "      nonnegative integer: the number of times we put item #K in the knapsack.\n",
+    "    \"\"\"\n",
     "    model = cp_model.CpModel()\n",
-    "    variables = [\n",
-    "        model.NewIntVar(0, total_size_max // size, '') for size in item_sizes\n",
-    "    ]\n",
+    "    variables = [model.NewIntVar(0, total_size_max // size, \"\") for size in item_sizes]\n",
     "    load = sum(variables[i] * size for i, size in enumerate(item_sizes))\n",
     "    model.AddLinearConstraint(load, total_size_min, total_size_max)\n",
     "\n",
@@ -148,52 +144,52 @@
     "    return solution_collector.combinations()\n",
     "\n",
     "\n",
-    "def AggregateItemCollectionsOptimally(item_collections, max_num_collections,\n",
-    "                                      ideal_item_ratios):\n",
+    "def AggregateItemCollectionsOptimally(\n",
+    "    item_collections, max_num_collections, ideal_item_ratios\n",
+    "):\n",
     "    \"\"\"Selects a set (with repetition) of combination of items optimally.\n",
     "\n",
-    "  Given a set of collections of N possible items (in each collection, an item\n",
-    "  may appear multiple times), a  given \"ideal breakdown of items\", and a\n",
-    "  maximum number of collections, this method finds the optimal way to\n",
-    "  aggregate the collections in order to:\n",
-    "  - maximize the overall number of items\n",
-    "  - while keeping the ratio of each item, among the overall selection, as close\n",
-    "    as possible to a given input ratio (which depends on the item).\n",
-    "  Each collection may be selected more than one time.\n",
+    "    Given a set of collections of N possible items (in each collection, an item\n",
+    "    may appear multiple times), a  given \"ideal breakdown of items\", and a\n",
+    "    maximum number of collections, this method finds the optimal way to\n",
+    "    aggregate the collections in order to:\n",
+    "    - maximize the overall number of items\n",
+    "    - while keeping the ratio of each item, among the overall selection, as close\n",
+    "      as possible to a given input ratio (which depends on the item).\n",
+    "    Each collection may be selected more than one time.\n",
     "\n",
-    "  Args:\n",
-    "    item_collections: a list of item collections. Each item collection is a list\n",
-    "      of integers [#item0, ..., #itemN-1], where #itemK is the number of times\n",
-    "      item #K appears in the collection, and N is the number of distinct items.\n",
-    "    max_num_collections: an integer, the maximum number of item collections that\n",
-    "      may be selected (counting repetitions of the same collection).\n",
-    "    ideal_item_ratios: A list of N float which sums to 1.0: the K-th element is\n",
-    "      the ideal ratio of item #K in the whole aggregated selection.\n",
+    "    Args:\n",
+    "      item_collections: a list of item collections. Each item collection is a list\n",
+    "        of integers [#item0, ..., #itemN-1], where #itemK is the number of times\n",
+    "        item #K appears in the collection, and N is the number of distinct items.\n",
+    "      max_num_collections: an integer, the maximum number of item collections that\n",
+    "        may be selected (counting repetitions of the same collection).\n",
+    "      ideal_item_ratios: A list of N float which sums to 1.0: the K-th element is\n",
+    "        the ideal ratio of item #K in the whole aggregated selection.\n",
     "\n",
-    "  Returns:\n",
-    "    A pair (objective value, list of pairs (item collection, num_selections)),\n",
-    "    where:\n",
-    "      - \"objective value\" is the value of the internal objective function used\n",
-    "        by the MIP Solver\n",
-    "      - Each \"item collection\" is an element of the input item_collections\n",
-    "      - and its associated \"num_selections\" is the number of times it was\n",
-    "        selected.\n",
-    "  \"\"\"\n",
-    "    solver = pywraplp.Solver.CreateSolver('SCIP')\n",
+    "    Returns:\n",
+    "      A pair (objective value, list of pairs (item collection, num_selections)),\n",
+    "      where:\n",
+    "        - \"objective value\" is the value of the internal objective function used\n",
+    "          by the MIP Solver\n",
+    "        - Each \"item collection\" is an element of the input item_collections\n",
+    "        - and its associated \"num_selections\" is the number of times it was\n",
+    "          selected.\n",
+    "    \"\"\"\n",
+    "    solver = pywraplp.Solver.CreateSolver(\"SCIP\")\n",
     "    if not solver:\n",
     "        return []\n",
     "    n = len(ideal_item_ratios)\n",
     "    num_distinct_collections = len(item_collections)\n",
     "    max_num_items_per_collection = 0\n",
     "    for template in item_collections:\n",
-    "        max_num_items_per_collection = max(max_num_items_per_collection,\n",
-    "                                           sum(template))\n",
+    "        max_num_items_per_collection = max(max_num_items_per_collection, sum(template))\n",
     "    upper_bound = max_num_items_per_collection * max_num_collections\n",
     "\n",
     "    # num_selections_of_collection[i] is an IntVar that represents the number\n",
     "    # of times that we will use collection #i in our global selection.\n",
     "    num_selections_of_collection = [\n",
-    "        solver.IntVar(0, max_num_collections, 's[%d]' % i)\n",
+    "        solver.IntVar(0, max_num_collections, \"s[%d]\" % i)\n",
     "        for i in range(num_distinct_collections)\n",
     "    ]\n",
     "\n",
@@ -201,19 +197,17 @@
     "    # aggregated over all selected collections. This is enforced with dedicated\n",
     "    # constraints that bind them with the num_selections_of_collection vars.\n",
     "    num_overall_item = [\n",
-    "        solver.IntVar(0, upper_bound, 'num_overall_item[%d]' % i)\n",
-    "        for i in range(n)\n",
+    "        solver.IntVar(0, upper_bound, \"num_overall_item[%d]\" % i) for i in range(n)\n",
     "    ]\n",
     "    for i in range(n):\n",
     "        ct = solver.Constraint(0.0, 0.0)\n",
     "        ct.SetCoefficient(num_overall_item[i], -1)\n",
     "        for j in range(num_distinct_collections):\n",
-    "            ct.SetCoefficient(num_selections_of_collection[j],\n",
-    "                              item_collections[j][i])\n",
+    "            ct.SetCoefficient(num_selections_of_collection[j], item_collections[j][i])\n",
     "\n",
     "    # Maintain the num_all_item variable as the sum of all num_overall_item\n",
     "    # variables.\n",
-    "    num_all_items = solver.IntVar(0, upper_bound, 'num_all_items')\n",
+    "    num_all_items = solver.IntVar(0, upper_bound, \"num_all_items\")\n",
     "    solver.Add(solver.Sum(num_overall_item) == num_all_items)\n",
     "\n",
     "    # Sets the total number of workers.\n",
@@ -221,15 +215,16 @@
     "\n",
     "    # Objective variables.\n",
     "    deviation_vars = [\n",
-    "        solver.NumVar(0, upper_bound, 'deviation_vars[%d]' % i)\n",
-    "        for i in range(n)\n",
+    "        solver.NumVar(0, upper_bound, \"deviation_vars[%d]\" % i) for i in range(n)\n",
     "    ]\n",
     "    for i in range(n):\n",
     "        deviation = deviation_vars[i]\n",
-    "        solver.Add(deviation >= num_overall_item[i] -\n",
-    "                   ideal_item_ratios[i] * num_all_items)\n",
-    "        solver.Add(deviation >= ideal_item_ratios[i] * num_all_items -\n",
-    "                   num_overall_item[i])\n",
+    "        solver.Add(\n",
+    "            deviation >= num_overall_item[i] - ideal_item_ratios[i] * num_all_items\n",
+    "        )\n",
+    "        solver.Add(\n",
+    "            deviation >= ideal_item_ratios[i] * num_all_items - num_overall_item[i]\n",
+    "        )\n",
     "\n",
     "    solver.Maximize(num_all_items - solver.Sum(deviation_vars))\n",
     "\n",
@@ -244,43 +239,57 @@
     "def GetOptimalSchedule(demand):\n",
     "    \"\"\"Computes the optimal schedule for the installation input.\n",
     "\n",
-    "  Args:\n",
-    "    demand: a list of \"appointment types\". Each \"appointment type\" is a triple\n",
-    "      (ideal_ratio_pct, name, duration_minutes), where ideal_ratio_pct is the\n",
-    "      ideal percentage (in [0..100.0]) of that type of appointment among all\n",
-    "      appointments scheduled.\n",
+    "    Args:\n",
+    "      demand: a list of \"appointment types\". Each \"appointment type\" is a triple\n",
+    "        (ideal_ratio_pct, name, duration_minutes), where ideal_ratio_pct is the\n",
+    "        ideal percentage (in [0..100.0]) of that type of appointment among all\n",
+    "        appointments scheduled.\n",
     "\n",
-    "  Returns:\n",
-    "    The same output type as EnumerateAllKnapsacksWithRepetition.\n",
-    "  \"\"\"\n",
+    "    Returns:\n",
+    "      The same output type as EnumerateAllKnapsacksWithRepetition.\n",
+    "    \"\"\"\n",
     "    combinations = EnumerateAllKnapsacksWithRepetition(\n",
-    "        [a[2] + _COMMUTE_TIME.value for a in demand], _LOAD_MIN.value,\n",
-    "        _LOAD_MAX.value)\n",
-    "    print(('Found %d possible day schedules ' % len(combinations) +\n",
-    "           '(i.e. combination of appointments filling up one worker\\'s day)'))\n",
+    "        [a[2] + _COMMUTE_TIME.value for a in demand], _LOAD_MIN.value, _LOAD_MAX.value\n",
+    "    )\n",
+    "    print(\n",
+    "        (\n",
+    "            \"Found %d possible day schedules \" % len(combinations)\n",
+    "            + \"(i.e. combination of appointments filling up one worker's day)\"\n",
+    "        )\n",
+    "    )\n",
     "\n",
     "    selection = AggregateItemCollectionsOptimally(\n",
-    "        combinations, _NUM_WORKERS.value, [a[0] / 100.0 for a in demand])\n",
+    "        combinations, _NUM_WORKERS.value, [a[0] / 100.0 for a in demand]\n",
+    "    )\n",
     "    output = []\n",
     "    for i in range(len(selection)):\n",
     "        if selection[i] != 0:\n",
-    "            output.append((selection[i], [(combinations[i][t], demand[t][1])\n",
-    "                                          for t in range(len(demand))\n",
-    "                                          if combinations[i][t] != 0]))\n",
+    "            output.append(\n",
+    "                (\n",
+    "                    selection[i],\n",
+    "                    [\n",
+    "                        (combinations[i][t], demand[t][1])\n",
+    "                        for t in range(len(demand))\n",
+    "                        if combinations[i][t] != 0\n",
+    "                    ],\n",
+    "                )\n",
+    "            )\n",
     "\n",
     "    return output\n",
     "\n",
     "\n",
     "def main(_):\n",
-    "    demand = [(45.0, 'Type1', 90), (30.0, 'Type2', 120), (25.0, 'Type3', 180)]\n",
-    "    print('*** input problem ***')\n",
-    "    print('Appointments: ')\n",
+    "    demand = [(45.0, \"Type1\", 90), (30.0, \"Type2\", 120), (25.0, \"Type3\", 180)]\n",
+    "    print(\"*** input problem ***\")\n",
+    "    print(\"Appointments: \")\n",
     "    for a in demand:\n",
-    "        print('   %.2f%% of %s : %d min' % (a[0], a[1], a[2]))\n",
-    "    print('Commute time = %d' % _COMMUTE_TIME.value)\n",
-    "    print('Acceptable duration of a work day = [%d..%d]' %\n",
-    "          (_LOAD_MIN.value, _LOAD_MAX.value))\n",
-    "    print('%d workers' % _NUM_WORKERS.value)\n",
+    "        print(\"   %.2f%% of %s : %d min\" % (a[0], a[1], a[2]))\n",
+    "    print(\"Commute time = %d\" % _COMMUTE_TIME.value)\n",
+    "    print(\n",
+    "        \"Acceptable duration of a work day = [%d..%d]\"\n",
+    "        % (_LOAD_MIN.value, _LOAD_MAX.value)\n",
+    "    )\n",
+    "    print(\"%d workers\" % _NUM_WORKERS.value)\n",
     "    selection = GetOptimalSchedule(demand)\n",
     "    print()\n",
     "    installed = 0\n",
@@ -288,27 +297,27 @@
     "    for a in demand:\n",
     "        installed_per_type[a[1]] = 0\n",
     "\n",
-    "    print('*** output solution ***')\n",
+    "    print(\"*** output solution ***\")\n",
     "    for template in selection:\n",
     "        num_instances = template[0]\n",
-    "        print('%d schedules with ' % num_instances)\n",
+    "        print(\"%d schedules with \" % num_instances)\n",
     "        for t in template[1]:\n",
     "            mult = t[0]\n",
-    "            print('   %d installation of type %s' % (mult, t[1]))\n",
+    "            print(\"   %d installation of type %s\" % (mult, t[1]))\n",
     "            installed += num_instances * mult\n",
     "            installed_per_type[t[1]] += num_instances * mult\n",
     "\n",
     "    print()\n",
-    "    print('%d installations planned' % installed)\n",
+    "    print(\"%d installations planned\" % installed)\n",
     "    for a in demand:\n",
     "        name = a[1]\n",
     "        per_type = installed_per_type[name]\n",
     "        if installed != 0:\n",
     "            print(\n",
-    "                f'   {per_type} ({per_type * 100.0 / installed}%) installations of type {name} planned'\n",
+    "                f\"   {per_type} ({per_type * 100.0 / installed}%) installations of type {name} planned\"\n",
     "            )\n",
     "        else:\n",
-    "            print(f'   {per_type} installations of type {name} planned')\n",
+    "            print(f\"   {per_type} installations of type {name} planned\")\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/examples/assignment_with_constraints_sat.ipynb b/examples/notebook/examples/assignment_with_constraints_sat.ipynb
index 2490af8d1f..341ebe2086 100644
--- a/examples/notebook/examples/assignment_with_constraints_sat.ipynb
+++ b/examples/notebook/examples/assignment_with_constraints_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Solve an assignment problem with combination constraints on workers.\n"
    ]
   },
@@ -89,19 +90,27 @@
     "def solve_assignment():\n",
     "    \"\"\"Solve the assignment problem.\"\"\"\n",
     "    # Data.\n",
-    "    cost = [[90, 76, 75, 70, 50, 74], [35, 85, 55, 65, 48, 101],\n",
-    "            [125, 95, 90, 105, 59, 120], [45, 110, 95, 115, 104, 83],\n",
-    "            [60, 105, 80, 75, 59, 62], [45, 65, 110, 95, 47, 31],\n",
-    "            [38, 51, 107, 41, 69, 99], [47, 85, 57, 71, 92, 77],\n",
-    "            [39, 63, 97, 49, 118, 56], [47, 101, 71, 60, 88, 109],\n",
-    "            [17, 39, 103, 64, 61, 92], [101, 45, 83, 59, 92, 27]]\n",
+    "    cost = [\n",
+    "        [90, 76, 75, 70, 50, 74],\n",
+    "        [35, 85, 55, 65, 48, 101],\n",
+    "        [125, 95, 90, 105, 59, 120],\n",
+    "        [45, 110, 95, 115, 104, 83],\n",
+    "        [60, 105, 80, 75, 59, 62],\n",
+    "        [45, 65, 110, 95, 47, 31],\n",
+    "        [38, 51, 107, 41, 69, 99],\n",
+    "        [47, 85, 57, 71, 92, 77],\n",
+    "        [39, 63, 97, 49, 118, 56],\n",
+    "        [47, 101, 71, 60, 88, 109],\n",
+    "        [17, 39, 103, 64, 61, 92],\n",
+    "        [101, 45, 83, 59, 92, 27],\n",
+    "    ]\n",
     "\n",
     "    group1 = [\n",
     "        [0, 0, 1, 1],  # Workers 2, 3\n",
     "        [0, 1, 0, 1],  # Workers 1, 3\n",
     "        [0, 1, 1, 0],  # Workers 1, 2\n",
     "        [1, 1, 0, 0],  # Workers 0, 1\n",
-    "        [1, 0, 1, 0]\n",
+    "        [1, 0, 1, 0],\n",
     "    ]  # Workers 0, 2\n",
     "\n",
     "    group2 = [\n",
@@ -109,7 +118,7 @@
     "        [0, 1, 0, 1],  # Workers 5, 7\n",
     "        [0, 1, 1, 0],  # Workers 5, 6\n",
     "        [1, 1, 0, 0],  # Workers 4, 5\n",
-    "        [1, 0, 0, 1]\n",
+    "        [1, 0, 0, 1],\n",
     "    ]  # Workers 4, 7\n",
     "\n",
     "    group3 = [\n",
@@ -117,7 +126,7 @@
     "        [0, 1, 0, 1],  # Workers 9, 11\n",
     "        [0, 1, 1, 0],  # Workers 9, 10\n",
     "        [1, 0, 1, 0],  # Workers 8, 10\n",
-    "        [1, 0, 0, 1]\n",
+    "        [1, 0, 0, 1],\n",
     "    ]  # Workers 8, 11\n",
     "\n",
     "    sizes = [10, 7, 3, 12, 15, 4, 11, 5]\n",
@@ -131,10 +140,10 @@
     "\n",
     "    model = cp_model.CpModel()\n",
     "    # Variables\n",
-    "    selected = [[model.NewBoolVar('x[%i,%i]' % (i, j))\n",
-    "                 for j in all_tasks]\n",
-    "                for i in all_workers]\n",
-    "    works = [model.NewBoolVar('works[%i]' % i) for i in all_workers]\n",
+    "    selected = [\n",
+    "        [model.NewBoolVar(\"x[%i,%i]\" % (i, j)) for j in all_tasks] for i in all_workers\n",
+    "    ]\n",
+    "    works = [model.NewBoolVar(\"works[%i]\" % i) for i in all_workers]\n",
     "\n",
     "    # Constraints\n",
     "\n",
@@ -148,47 +157,43 @@
     "\n",
     "    # Total task size for each worker is at most total_size_max\n",
     "    for i in all_workers:\n",
-    "        model.Add(\n",
-    "            sum(sizes[j] * selected[i][j] for j in all_tasks) <= total_size_max)\n",
+    "        model.Add(sum(sizes[j] * selected[i][j] for j in all_tasks) <= total_size_max)\n",
     "\n",
     "    # Group constraints.\n",
-    "    model.AddAllowedAssignments([works[0], works[1], works[2], works[3]],\n",
-    "                                group1)\n",
-    "    model.AddAllowedAssignments([works[4], works[5], works[6], works[7]],\n",
-    "                                group2)\n",
-    "    model.AddAllowedAssignments([works[8], works[9], works[10], works[11]],\n",
-    "                                group3)\n",
+    "    model.AddAllowedAssignments([works[0], works[1], works[2], works[3]], group1)\n",
+    "    model.AddAllowedAssignments([works[4], works[5], works[6], works[7]], group2)\n",
+    "    model.AddAllowedAssignments([works[8], works[9], works[10], works[11]], group3)\n",
     "\n",
     "    # Objective\n",
     "    model.Minimize(\n",
-    "        sum(selected[i][j] * cost[i][j]\n",
-    "            for j in all_tasks\n",
-    "            for i in all_workers))\n",
+    "        sum(selected[i][j] * cost[i][j] for j in all_tasks for i in all_workers)\n",
+    "    )\n",
     "\n",
     "    # Solve and output solution.\n",
     "    solver = cp_model.CpSolver()\n",
     "    status = solver.Solve(model)\n",
     "\n",
     "    if status == cp_model.OPTIMAL:\n",
-    "        print('Total cost = %i' % solver.ObjectiveValue())\n",
+    "        print(\"Total cost = %i\" % solver.ObjectiveValue())\n",
     "        print()\n",
     "        for i in all_workers:\n",
     "            for j in all_tasks:\n",
     "                if solver.BooleanValue(selected[i][j]):\n",
-    "                    print('Worker ', i, ' assigned to task ', j, '  Cost = ',\n",
-    "                          cost[i][j])\n",
+    "                    print(\n",
+    "                        \"Worker \", i, \" assigned to task \", j, \"  Cost = \", cost[i][j]\n",
+    "                    )\n",
     "\n",
     "        print()\n",
     "\n",
-    "    print('Statistics')\n",
-    "    print('  - conflicts : %i' % solver.NumConflicts())\n",
-    "    print('  - branches  : %i' % solver.NumBranches())\n",
-    "    print('  - wall time : %f s' % solver.WallTime())\n",
+    "    print(\"Statistics\")\n",
+    "    print(\"  - conflicts : %i\" % solver.NumConflicts())\n",
+    "    print(\"  - branches  : %i\" % solver.NumBranches())\n",
+    "    print(\"  - wall time : %f s\" % solver.WallTime())\n",
     "\n",
     "\n",
     "def main(argv: Sequence[str]) -> None:\n",
     "    if len(argv) > 1:\n",
-    "        raise app.UsageError('Too many command-line arguments.')\n",
+    "        raise app.UsageError(\"Too many command-line arguments.\")\n",
     "    solve_assignment()\n",
     "\n",
     "\n",
diff --git a/examples/notebook/examples/balance_group_sat.ipynb b/examples/notebook/examples/balance_group_sat.ipynb
index db8e1b4c49..3d2cda0335 100644
--- a/examples/notebook/examples/balance_group_sat.ipynb
+++ b/examples/notebook/examples/balance_group_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "We are trying to group items in equal sized groups.\n",
     "\n",
     "Each item has a color and a value. We want the sum of values of each group to\n",
@@ -105,10 +106,10 @@
     "        self.__item_in_group = item_in_group\n",
     "\n",
     "    def on_solution_callback(self):\n",
-    "        print('Solution %i' % self.__solution_count)\n",
+    "        print(\"Solution %i\" % self.__solution_count)\n",
     "        self.__solution_count += 1\n",
     "\n",
-    "        print('  objective value = %i' % self.ObjectiveValue())\n",
+    "        print(\"  objective value = %i\" % self.ObjectiveValue())\n",
     "        groups = {}\n",
     "        sums = {}\n",
     "        for g in self.__all_groups:\n",
@@ -121,19 +122,19 @@
     "\n",
     "        for g in self.__all_groups:\n",
     "            group = groups[g]\n",
-    "            print('group %i: sum = %0.2f [' % (g, sums[g]), end='')\n",
+    "            print(\"group %i: sum = %0.2f [\" % (g, sums[g]), end=\"\")\n",
     "            for item in group:\n",
     "                value = self.__values[item]\n",
     "                color = self.__colors[item]\n",
-    "                print(' (%i, %i, %i)' % (item, value, color), end='')\n",
-    "            print(']')\n",
+    "                print(\" (%i, %i, %i)\" % (item, value, color), end=\"\")\n",
+    "            print(\"]\")\n",
     "\n",
     "\n",
     "def main(argv: Sequence[str]) -> None:\n",
     "    \"\"\"Solves a group balancing problem.\"\"\"\n",
     "\n",
     "    if len(argv) > 1:\n",
-    "        raise app.UsageError('Too many command-line arguments.')\n",
+    "        raise app.UsageError(\"Too many command-line arguments.\")\n",
     "    # Data.\n",
     "    num_groups = 10\n",
     "    num_items = 100\n",
@@ -162,9 +163,11 @@
     "            if colors[i] == c:\n",
     "                items_per_color[c].append(i)\n",
     "\n",
-    "    print('Model has %i items, %i groups, and %i colors' %\n",
-    "          (num_items, num_groups, num_colors))\n",
-    "    print('  average sum per group = %i' % average_sum_per_group)\n",
+    "    print(\n",
+    "        \"Model has %i items, %i groups, and %i colors\"\n",
+    "        % (num_items, num_groups, num_colors)\n",
+    "    )\n",
+    "    print(\"  average sum per group = %i\" % average_sum_per_group)\n",
     "\n",
     "    # Model.\n",
     "\n",
@@ -173,43 +176,42 @@
     "    item_in_group = {}\n",
     "    for i in all_items:\n",
     "        for g in all_groups:\n",
-    "            item_in_group[(i, g)] = model.NewBoolVar('item %d in group %d' %\n",
-    "                                                     (i, g))\n",
+    "            item_in_group[(i, g)] = model.NewBoolVar(\"item %d in group %d\" % (i, g))\n",
     "\n",
     "    # Each group must have the same size.\n",
     "    for g in all_groups:\n",
-    "        model.Add(\n",
-    "            sum(item_in_group[(i, g)]\n",
-    "                for i in all_items) == num_items_per_group)\n",
+    "        model.Add(sum(item_in_group[(i, g)] for i in all_items) == num_items_per_group)\n",
     "\n",
     "    # One item must belong to exactly one group.\n",
     "    for i in all_items:\n",
     "        model.Add(sum(item_in_group[(i, g)] for g in all_groups) == 1)\n",
     "\n",
     "    # The deviation of the sum of each items in a group against the average.\n",
-    "    e = model.NewIntVar(0, 550, 'epsilon')\n",
+    "    e = model.NewIntVar(0, 550, \"epsilon\")\n",
     "\n",
     "    # Constrain the sum of values in one group around the average sum per group.\n",
     "    for g in all_groups:\n",
     "        model.Add(\n",
-    "            sum(item_in_group[(i, g)] * values[i]\n",
-    "                for i in all_items) <= average_sum_per_group + e)\n",
+    "            sum(item_in_group[(i, g)] * values[i] for i in all_items)\n",
+    "            <= average_sum_per_group + e\n",
+    "        )\n",
     "        model.Add(\n",
-    "            sum(item_in_group[(i, g)] * values[i]\n",
-    "                for i in all_items) >= average_sum_per_group - e)\n",
+    "            sum(item_in_group[(i, g)] * values[i] for i in all_items)\n",
+    "            >= average_sum_per_group - e\n",
+    "        )\n",
     "\n",
     "    # color_in_group variables.\n",
     "    color_in_group = {}\n",
     "    for g in all_groups:\n",
     "        for c in all_colors:\n",
     "            color_in_group[(c, g)] = model.NewBoolVar(\n",
-    "                'color %d is in group %d' % (c, g))\n",
+    "                \"color %d is in group %d\" % (c, g)\n",
+    "            )\n",
     "\n",
     "    # Item is in a group implies its color is in that group.\n",
     "    for i in all_items:\n",
     "        for g in all_groups:\n",
-    "            model.AddImplication(item_in_group[(i, g)],\n",
-    "                                 color_in_group[(colors[i], g)])\n",
+    "            model.AddImplication(item_in_group[(i, g)], color_in_group[(colors[i], g)])\n",
     "\n",
     "    # If a color is in a group, it must contains at least\n",
     "    # min_items_of_same_color_per_group items from that color.\n",
@@ -217,8 +219,9 @@
     "        for g in all_groups:\n",
     "            literal = color_in_group[(c, g)]\n",
     "            model.Add(\n",
-    "                sum(item_in_group[(i, g)] for i in items_per_color[c]) >=\n",
-    "                min_items_of_same_color_per_group).OnlyEnforceIf(literal)\n",
+    "                sum(item_in_group[(i, g)] for i in items_per_color[c])\n",
+    "                >= min_items_of_same_color_per_group\n",
+    "            ).OnlyEnforceIf(literal)\n",
     "\n",
     "    # Compute the maximum number of colors in a group.\n",
     "    max_color = num_items_per_group // min_items_of_same_color_per_group\n",
@@ -226,8 +229,7 @@
     "    # Redundant constraint, it helps with solving time.\n",
     "    if max_color < num_colors:\n",
     "        for g in all_groups:\n",
-    "            model.Add(\n",
-    "                sum(color_in_group[(c, g)] for c in all_colors) <= max_color)\n",
+    "            model.Add(sum(color_in_group[(c, g)] for c in all_colors) <= max_color)\n",
     "\n",
     "    # Minimize epsilon\n",
     "    model.Minimize(e)\n",
@@ -235,18 +237,19 @@
     "    solver = cp_model.CpSolver()\n",
     "    # solver.parameters.log_search_progress = True\n",
     "    solver.parameters.num_workers = 16\n",
-    "    solution_printer = SolutionPrinter(values, colors, all_groups, all_items,\n",
-    "                                       item_in_group)\n",
+    "    solution_printer = SolutionPrinter(\n",
+    "        values, colors, all_groups, all_items, item_in_group\n",
+    "    )\n",
     "    status = solver.Solve(model, solution_printer)\n",
     "\n",
     "    if status == cp_model.OPTIMAL:\n",
-    "        print('Optimal epsilon: %i' % solver.ObjectiveValue())\n",
-    "        print('Statistics')\n",
-    "        print('  - conflicts : %i' % solver.NumConflicts())\n",
-    "        print('  - branches  : %i' % solver.NumBranches())\n",
-    "        print('  - wall time : %f s' % solver.WallTime())\n",
+    "        print(\"Optimal epsilon: %i\" % solver.ObjectiveValue())\n",
+    "        print(\"Statistics\")\n",
+    "        print(\"  - conflicts : %i\" % solver.NumConflicts())\n",
+    "        print(\"  - branches  : %i\" % solver.NumBranches())\n",
+    "        print(\"  - wall time : %f s\" % solver.WallTime())\n",
     "    else:\n",
-    "        print('No solution found')\n",
+    "        print(\"No solution found\")\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/examples/bus_driver_scheduling_sat.ipynb b/examples/notebook/examples/bus_driver_scheduling_sat.ipynb
index 53e475dc11..5f05a9503e 100644
--- a/examples/notebook/examples/bus_driver_scheduling_sat.ipynb
+++ b/examples/notebook/examples/bus_driver_scheduling_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "This model implements a bus driver scheduling problem.\n",
     "\n",
     "Constraints:\n",
@@ -99,13 +100,16 @@
     "from ortools.sat.python import cp_model\n",
     "\n",
     "_OUTPUT_PROTO = flags.DEFINE_string(\n",
-    "    'output_proto', '', 'Output file to write the cp_model proto to.')\n",
+    "    \"output_proto\", \"\", \"Output file to write the cp_model proto to.\"\n",
+    ")\n",
     "_PARAMS = flags.DEFINE_string(\n",
-    "    'params',\n",
-    "    'num_search_workers:16,log_search_progress:true,max_time_in_seconds:30',\n",
-    "    'Sat solver parameters.')\n",
-    "_INSTANCE = flags.DEFINE_integer('instance', 0,\n",
-    "                                 'Instance to select (0, 1, 2, 3).', 0, 3)\n",
+    "    \"params\",\n",
+    "    \"num_search_workers:16,log_search_progress:true,max_time_in_seconds:30\",\n",
+    "    \"Sat solver parameters.\",\n",
+    ")\n",
+    "_INSTANCE = flags.DEFINE_integer(\n",
+    "    \"instance\", 0, \"Instance to select (0, 1, 2, 3).\", 0, 3\n",
+    ")\n",
     "\n",
     "SAMPLE_SHIFTS_TINY = [\n",
     "    #\n",
@@ -117,33 +121,33 @@
     "    # - shift end minute\n",
     "    # - shift duration in minutes\n",
     "    #\n",
-    "    [1, '08:00', '09:05', 480, 545, 65],\n",
-    "    [2, '08:00', '08:35', 480, 515, 35],\n",
-    "    [3, '08:11', '09:41', 491, 581, 90],\n",
-    "    [4, '08:28', '08:50', 508, 530, 22],\n",
-    "    [5, '08:35', '08:45', 515, 525, 10],\n",
-    "    [6, '08:40', '08:50', 520, 530, 10],\n",
-    "    [7, '09:03', '10:28', 543, 628, 85],\n",
-    "    [8, '09:23', '09:49', 563, 589, 26],\n",
-    "    [9, '09:30', '09:40', 570, 580, 10],\n",
-    "    [10, '09:57', '10:20', 597, 620, 23],\n",
-    "    [11, '10:09', '11:03', 609, 663, 54],\n",
-    "    [12, '10:20', '10:30', 620, 630, 10],\n",
-    "    [13, '11:00', '11:10', 660, 670, 10],\n",
-    "    [14, '11:45', '12:24', 705, 744, 39],\n",
-    "    [15, '12:18', '13:00', 738, 780, 42],\n",
-    "    [16, '13:18', '14:44', 798, 884, 86],\n",
-    "    [17, '13:53', '14:49', 833, 889, 56],\n",
-    "    [18, '14:03', '14:50', 843, 890, 47],\n",
-    "    [19, '14:28', '15:15', 868, 915, 47],\n",
-    "    [20, '14:30', '15:41', 870, 941, 71],\n",
-    "    [21, '14:48', '15:35', 888, 935, 47],\n",
-    "    [22, '15:03', '15:50', 903, 950, 47],\n",
-    "    [23, '15:28', '16:54', 928, 1014, 86],\n",
-    "    [24, '15:38', '16:25', 938, 985, 47],\n",
-    "    [25, '15:40', '15:56', 940, 956, 16],\n",
-    "    [26, '15:58', '16:45', 958, 1005, 47],\n",
-    "    [27, '16:04', '17:30', 964, 1050, 86],\n",
+    "    [1, \"08:00\", \"09:05\", 480, 545, 65],\n",
+    "    [2, \"08:00\", \"08:35\", 480, 515, 35],\n",
+    "    [3, \"08:11\", \"09:41\", 491, 581, 90],\n",
+    "    [4, \"08:28\", \"08:50\", 508, 530, 22],\n",
+    "    [5, \"08:35\", \"08:45\", 515, 525, 10],\n",
+    "    [6, \"08:40\", \"08:50\", 520, 530, 10],\n",
+    "    [7, \"09:03\", \"10:28\", 543, 628, 85],\n",
+    "    [8, \"09:23\", \"09:49\", 563, 589, 26],\n",
+    "    [9, \"09:30\", \"09:40\", 570, 580, 10],\n",
+    "    [10, \"09:57\", \"10:20\", 597, 620, 23],\n",
+    "    [11, \"10:09\", \"11:03\", 609, 663, 54],\n",
+    "    [12, \"10:20\", \"10:30\", 620, 630, 10],\n",
+    "    [13, \"11:00\", \"11:10\", 660, 670, 10],\n",
+    "    [14, \"11:45\", \"12:24\", 705, 744, 39],\n",
+    "    [15, \"12:18\", \"13:00\", 738, 780, 42],\n",
+    "    [16, \"13:18\", \"14:44\", 798, 884, 86],\n",
+    "    [17, \"13:53\", \"14:49\", 833, 889, 56],\n",
+    "    [18, \"14:03\", \"14:50\", 843, 890, 47],\n",
+    "    [19, \"14:28\", \"15:15\", 868, 915, 47],\n",
+    "    [20, \"14:30\", \"15:41\", 870, 941, 71],\n",
+    "    [21, \"14:48\", \"15:35\", 888, 935, 47],\n",
+    "    [22, \"15:03\", \"15:50\", 903, 950, 47],\n",
+    "    [23, \"15:28\", \"16:54\", 928, 1014, 86],\n",
+    "    [24, \"15:38\", \"16:25\", 938, 985, 47],\n",
+    "    [25, \"15:40\", \"15:56\", 940, 956, 16],\n",
+    "    [26, \"15:58\", \"16:45\", 958, 1005, 47],\n",
+    "    [27, \"16:04\", \"17:30\", 964, 1050, 86],\n",
     "]  # yapf:disable\n",
     "\n",
     "SAMPLE_SHIFTS_SMALL = [\n",
@@ -156,1643 +160,1642 @@
     "    # - shift end minute\n",
     "    # - shift duration in minutes\n",
     "    #\n",
-    "    [0, '05:18', '06:00', 318, 360, 42],\n",
-    "    [1, '05:26', '06:08', 326, 368, 42],\n",
-    "    [2, '05:40', '05:56', 340, 356, 16],\n",
-    "    [3, '06:06', '06:51', 366, 411, 45],\n",
-    "    [4, '06:40', '07:52', 400, 472, 72],\n",
-    "    [5, '06:42', '07:13', 402, 433, 31],\n",
-    "    [6, '06:48', '08:15', 408, 495, 87],\n",
-    "    [7, '06:59', '08:07', 419, 487, 68],\n",
-    "    [8, '07:20', '07:36', 440, 456, 16],\n",
-    "    [9, '07:35', '08:22', 455, 502, 47],\n",
-    "    [10, '07:50', '08:55', 470, 535, 65],\n",
-    "    [11, '08:00', '09:05', 480, 545, 65],\n",
-    "    [12, '08:00', '08:35', 480, 515, 35],\n",
-    "    [13, '08:11', '09:41', 491, 581, 90],\n",
-    "    [14, '08:28', '08:50', 508, 530, 22],\n",
-    "    [15, '08:35', '08:45', 515, 525, 10],\n",
-    "    [16, '08:40', '08:50', 520, 530, 10],\n",
-    "    [17, '09:03', '10:28', 543, 628, 85],\n",
-    "    [18, '09:23', '09:49', 563, 589, 26],\n",
-    "    [19, '09:30', '09:40', 570, 580, 10],\n",
-    "    [20, '09:57', '10:20', 597, 620, 23],\n",
-    "    [21, '10:09', '11:03', 609, 663, 54],\n",
-    "    [22, '10:20', '10:30', 620, 630, 10],\n",
-    "    [23, '11:00', '11:10', 660, 670, 10],\n",
-    "    [24, '11:45', '12:24', 705, 744, 39],\n",
-    "    [25, '12:18', '13:00', 738, 780, 42],\n",
-    "    [26, '13:18', '14:44', 798, 884, 86],\n",
-    "    [27, '13:53', '14:49', 833, 889, 56],\n",
-    "    [28, '14:03', '14:50', 843, 890, 47],\n",
-    "    [29, '14:28', '15:15', 868, 915, 47],\n",
-    "    [30, '14:30', '15:41', 870, 941, 71],\n",
-    "    [31, '14:48', '15:35', 888, 935, 47],\n",
-    "    [32, '15:03', '15:50', 903, 950, 47],\n",
-    "    [33, '15:28', '16:54', 928, 1014, 86],\n",
-    "    [34, '15:38', '16:25', 938, 985, 47],\n",
-    "    [35, '15:40', '15:56', 940, 956, 16],\n",
-    "    [36, '15:58', '16:45', 958, 1005, 47],\n",
-    "    [37, '16:04', '17:30', 964, 1050, 86],\n",
-    "    [38, '16:28', '17:15', 988, 1035, 47],\n",
-    "    [39, '16:36', '17:21', 996, 1041, 45],\n",
-    "    [40, '16:50', '17:00', 1010, 1020, 10],\n",
-    "    [41, '16:54', '18:20', 1014, 1100, 86],\n",
-    "    [42, '17:01', '17:13', 1021, 1033, 12],\n",
-    "    [43, '17:19', '18:31', 1039, 1111, 72],\n",
-    "    [44, '17:23', '18:10', 1043, 1090, 47],\n",
-    "    [45, '17:34', '18:15', 1054, 1095, 41],\n",
-    "    [46, '18:04', '19:29', 1084, 1169, 85],\n",
-    "    [47, '18:34', '19:58', 1114, 1198, 84],\n",
-    "    [48, '19:56', '20:34', 1196, 1234, 38],\n",
-    "    [49, '20:05', '20:48', 1205, 1248, 43]\n",
+    "    [0, \"05:18\", \"06:00\", 318, 360, 42],\n",
+    "    [1, \"05:26\", \"06:08\", 326, 368, 42],\n",
+    "    [2, \"05:40\", \"05:56\", 340, 356, 16],\n",
+    "    [3, \"06:06\", \"06:51\", 366, 411, 45],\n",
+    "    [4, \"06:40\", \"07:52\", 400, 472, 72],\n",
+    "    [5, \"06:42\", \"07:13\", 402, 433, 31],\n",
+    "    [6, \"06:48\", \"08:15\", 408, 495, 87],\n",
+    "    [7, \"06:59\", \"08:07\", 419, 487, 68],\n",
+    "    [8, \"07:20\", \"07:36\", 440, 456, 16],\n",
+    "    [9, \"07:35\", \"08:22\", 455, 502, 47],\n",
+    "    [10, \"07:50\", \"08:55\", 470, 535, 65],\n",
+    "    [11, \"08:00\", \"09:05\", 480, 545, 65],\n",
+    "    [12, \"08:00\", \"08:35\", 480, 515, 35],\n",
+    "    [13, \"08:11\", \"09:41\", 491, 581, 90],\n",
+    "    [14, \"08:28\", \"08:50\", 508, 530, 22],\n",
+    "    [15, \"08:35\", \"08:45\", 515, 525, 10],\n",
+    "    [16, \"08:40\", \"08:50\", 520, 530, 10],\n",
+    "    [17, \"09:03\", \"10:28\", 543, 628, 85],\n",
+    "    [18, \"09:23\", \"09:49\", 563, 589, 26],\n",
+    "    [19, \"09:30\", \"09:40\", 570, 580, 10],\n",
+    "    [20, \"09:57\", \"10:20\", 597, 620, 23],\n",
+    "    [21, \"10:09\", \"11:03\", 609, 663, 54],\n",
+    "    [22, \"10:20\", \"10:30\", 620, 630, 10],\n",
+    "    [23, \"11:00\", \"11:10\", 660, 670, 10],\n",
+    "    [24, \"11:45\", \"12:24\", 705, 744, 39],\n",
+    "    [25, \"12:18\", \"13:00\", 738, 780, 42],\n",
+    "    [26, \"13:18\", \"14:44\", 798, 884, 86],\n",
+    "    [27, \"13:53\", \"14:49\", 833, 889, 56],\n",
+    "    [28, \"14:03\", \"14:50\", 843, 890, 47],\n",
+    "    [29, \"14:28\", \"15:15\", 868, 915, 47],\n",
+    "    [30, \"14:30\", \"15:41\", 870, 941, 71],\n",
+    "    [31, \"14:48\", \"15:35\", 888, 935, 47],\n",
+    "    [32, \"15:03\", \"15:50\", 903, 950, 47],\n",
+    "    [33, \"15:28\", \"16:54\", 928, 1014, 86],\n",
+    "    [34, \"15:38\", \"16:25\", 938, 985, 47],\n",
+    "    [35, \"15:40\", \"15:56\", 940, 956, 16],\n",
+    "    [36, \"15:58\", \"16:45\", 958, 1005, 47],\n",
+    "    [37, \"16:04\", \"17:30\", 964, 1050, 86],\n",
+    "    [38, \"16:28\", \"17:15\", 988, 1035, 47],\n",
+    "    [39, \"16:36\", \"17:21\", 996, 1041, 45],\n",
+    "    [40, \"16:50\", \"17:00\", 1010, 1020, 10],\n",
+    "    [41, \"16:54\", \"18:20\", 1014, 1100, 86],\n",
+    "    [42, \"17:01\", \"17:13\", 1021, 1033, 12],\n",
+    "    [43, \"17:19\", \"18:31\", 1039, 1111, 72],\n",
+    "    [44, \"17:23\", \"18:10\", 1043, 1090, 47],\n",
+    "    [45, \"17:34\", \"18:15\", 1054, 1095, 41],\n",
+    "    [46, \"18:04\", \"19:29\", 1084, 1169, 85],\n",
+    "    [47, \"18:34\", \"19:58\", 1114, 1198, 84],\n",
+    "    [48, \"19:56\", \"20:34\", 1196, 1234, 38],\n",
+    "    [49, \"20:05\", \"20:48\", 1205, 1248, 43],\n",
     "]  # yapf:disable\n",
     "\n",
     "SAMPLE_SHIFTS_MEDIUM = [\n",
-    "    [0, '04:30', '04:53', 270, 293, 23],\n",
-    "    [1, '04:46', '04:56', 286, 296, 10],\n",
-    "    [2, '04:52', '05:56', 292, 356, 64],\n",
-    "    [3, '04:53', '05:23', 293, 323, 30],\n",
-    "    [4, '05:07', '05:44', 307, 344, 37],\n",
-    "    [5, '05:10', '06:06', 310, 366, 56],\n",
-    "    [6, '05:18', '06:03', 318, 363, 45],\n",
-    "    [7, '05:30', '05:40', 330, 340, 10],\n",
-    "    [8, '05:30', '05:40', 330, 340, 10],\n",
-    "    [9, '05:33', '06:15', 333, 375, 42],\n",
-    "    [10, '05:40', '05:50', 340, 350, 10],\n",
-    "    [11, '05:43', '06:08', 343, 368, 25],\n",
-    "    [12, '05:54', '07:20', 354, 440, 86],\n",
-    "    [13, '06:04', '06:37', 364, 397, 33],\n",
-    "    [14, '06:13', '06:58', 373, 418, 45],\n",
-    "    [15, '06:14', '07:40', 374, 460, 86],\n",
-    "    [16, '06:15', '07:15', 375, 435, 60],\n",
-    "    [17, '06:16', '06:26', 376, 386, 10],\n",
-    "    [18, '06:17', '06:34', 377, 394, 17],\n",
-    "    [19, '06:20', '06:36', 380, 396, 16],\n",
-    "    [20, '06:22', '07:06', 382, 426, 44],\n",
-    "    [21, '06:24', '07:50', 384, 470, 86],\n",
-    "    [22, '06:27', '06:44', 387, 404, 17],\n",
-    "    [23, '06:30', '06:40', 390, 400, 10],\n",
-    "    [24, '06:31', '06:43', 391, 403, 12],\n",
-    "    [25, '06:33', '07:53', 393, 473, 80],\n",
-    "    [26, '06:34', '07:09', 394, 429, 35],\n",
-    "    [27, '06:40', '06:56', 400, 416, 16],\n",
-    "    [28, '06:44', '07:17', 404, 437, 33],\n",
-    "    [29, '06:46', '06:58', 406, 418, 12],\n",
-    "    [30, '06:49', '07:43', 409, 463, 54],\n",
-    "    [31, '06:50', '07:05', 410, 425, 15],\n",
-    "    [32, '06:52', '07:36', 412, 456, 44],\n",
-    "    [33, '06:54', '07:27', 414, 447, 33],\n",
-    "    [34, '06:56', '08:23', 416, 503, 87],\n",
-    "    [35, '07:04', '07:44', 424, 464, 40],\n",
-    "    [36, '07:11', '08:36', 431, 516, 85],\n",
-    "    [37, '07:17', '07:35', 437, 455, 18],\n",
-    "    [38, '07:22', '08:06', 442, 486, 44],\n",
-    "    [39, '07:27', '08:15', 447, 495, 48],\n",
-    "    [40, '07:35', '07:45', 455, 465, 10],\n",
-    "    [41, '07:43', '08:08', 463, 488, 25],\n",
-    "    [42, '07:50', '08:37', 470, 517, 47],\n",
-    "    [43, '07:58', '08:45', 478, 525, 47],\n",
-    "    [44, '08:00', '08:35', 480, 515, 35],\n",
-    "    [45, '08:06', '08:51', 486, 531, 45],\n",
-    "    [46, '08:10', '08:45', 490, 525, 35],\n",
-    "    [47, '08:15', '08:30', 495, 510, 15],\n",
-    "    [48, '08:16', '09:00', 496, 540, 44],\n",
-    "    [49, '08:18', '09:16', 498, 556, 58],\n",
-    "    [50, '08:20', '08:36', 500, 516, 16],\n",
-    "    [51, '08:27', '09:07', 507, 547, 40],\n",
-    "    [52, '08:30', '08:45', 510, 525, 15],\n",
-    "    [53, '08:35', '09:15', 515, 555, 40],\n",
-    "    [54, '08:46', '09:30', 526, 570, 44],\n",
-    "    [55, '08:51', '09:17', 531, 557, 26],\n",
-    "    [56, '08:55', '09:15', 535, 555, 20],\n",
-    "    [57, '08:58', '09:38', 538, 578, 40],\n",
-    "    [58, '09:00', '09:35', 540, 575, 35],\n",
-    "    [59, '09:00', '09:16', 540, 556, 16],\n",
-    "    [60, '09:20', '09:36', 560, 576, 16],\n",
-    "    [61, '09:31', '09:43', 571, 583, 12],\n",
-    "    [62, '09:33', '10:15', 573, 615, 42],\n",
-    "    [63, '09:54', '10:05', 594, 605, 11],\n",
-    "    [64, '10:11', '10:38', 611, 638, 27],\n",
-    "    [65, '10:18', '11:00', 618, 660, 42],\n",
-    "    [66, '10:21', '10:47', 621, 647, 26],\n",
-    "    [67, '10:25', '11:04', 625, 664, 39],\n",
-    "    [68, '10:26', '11:08', 626, 668, 42],\n",
-    "    [69, '10:44', '12:11', 644, 731, 87],\n",
-    "    [70, '11:00', '11:16', 660, 676, 16],\n",
-    "    [71, '11:15', '11:54', 675, 714, 39],\n",
-    "    [72, '11:16', '11:28', 676, 688, 12],\n",
-    "    [73, '11:20', '11:30', 680, 690, 10],\n",
-    "    [74, '11:21', '11:47', 681, 707, 26],\n",
-    "    [75, '11:25', '12:04', 685, 724, 39],\n",
-    "    [76, '11:34', '11:45', 694, 705, 11],\n",
-    "    [77, '11:35', '12:14', 695, 734, 39],\n",
-    "    [78, '11:41', '12:23', 701, 743, 42],\n",
-    "    [79, '11:44', '12:35', 704, 755, 51],\n",
-    "    [80, '11:46', '11:58', 706, 718, 12],\n",
-    "    [81, '12:00', '12:10', 720, 730, 10],\n",
-    "    [82, '12:04', '12:15', 724, 735, 11],\n",
-    "    [83, '12:04', '13:04', 724, 784, 60],\n",
-    "    [84, '12:11', '12:38', 731, 758, 27],\n",
-    "    [85, '12:15', '12:54', 735, 774, 39],\n",
-    "    [86, '12:25', '13:10', 745, 790, 45],\n",
-    "    [87, '12:30', '12:40', 750, 760, 10],\n",
-    "    [88, '12:34', '13:58', 754, 838, 84],\n",
-    "    [89, '12:38', '13:25', 758, 805, 47],\n",
-    "    [90, '12:48', '13:35', 768, 815, 47],\n",
-    "    [91, '13:00', '13:16', 780, 796, 16],\n",
-    "    [92, '13:05', '13:44', 785, 824, 39],\n",
-    "    [93, '13:08', '13:55', 788, 835, 47],\n",
-    "    [94, '13:14', '14:38', 794, 878, 84],\n",
-    "    [95, '13:23', '13:49', 803, 829, 26],\n",
-    "    [96, '13:25', '14:04', 805, 844, 39],\n",
-    "    [97, '13:28', '14:54', 808, 894, 86],\n",
-    "    [98, '13:31', '13:43', 811, 823, 12],\n",
-    "    [99, '13:34', '14:58', 814, 898, 84],\n",
-    "    [100, '13:38', '14:25', 818, 865, 47],\n",
-    "    [101, '13:38', '15:04', 818, 904, 86],\n",
-    "    [102, '13:39', '14:33', 819, 873, 54],\n",
-    "    [103, '13:40', '13:50', 820, 830, 10],\n",
-    "    [104, '13:43', '14:10', 823, 850, 27],\n",
-    "    [105, '13:48', '14:35', 828, 875, 47],\n",
-    "    [106, '13:48', '14:35', 828, 875, 47],\n",
-    "    [107, '13:53', '14:40', 833, 880, 47],\n",
-    "    [108, '13:58', '15:24', 838, 924, 86],\n",
-    "    [109, '13:58', '14:25', 838, 865, 27],\n",
-    "    [110, '14:00', '14:16', 840, 856, 16],\n",
-    "    [111, '14:13', '15:00', 853, 900, 47],\n",
-    "    [112, '14:20', '15:31', 860, 931, 71],\n",
-    "    [113, '14:25', '15:02', 865, 902, 37],\n",
-    "    [114, '14:34', '14:45', 874, 885, 11],\n",
-    "    [115, '14:40', '15:51', 880, 951, 71],\n",
-    "    [116, '14:40', '14:56', 880, 896, 16],\n",
-    "    [117, '14:46', '14:58', 886, 898, 12],\n",
-    "    [118, '14:49', '15:43', 889, 943, 54],\n",
-    "    [119, '14:52', '15:21', 892, 921, 29],\n",
-    "    [120, '14:58', '16:24', 898, 984, 86],\n",
-    "    [121, '14:59', '15:53', 899, 953, 54],\n",
-    "    [122, '15:00', '15:10', 900, 910, 10],\n",
-    "    [123, '15:00', '15:35', 900, 935, 35],\n",
-    "    [124, '15:08', '15:45', 908, 945, 37],\n",
-    "    [125, '15:12', '15:36', 912, 936, 24],\n",
-    "    [126, '15:18', '16:05', 918, 965, 47],\n",
-    "    [127, '15:24', '16:05', 924, 965, 41],\n",
-    "    [128, '15:31', '15:43', 931, 943, 12],\n",
-    "    [129, '15:35', '15:54', 935, 954, 19],\n",
-    "    [130, '15:36', '16:21', 936, 981, 45],\n",
-    "    [131, '15:39', '16:33', 939, 993, 54],\n",
-    "    [132, '15:48', '16:35', 948, 995, 47],\n",
-    "    [133, '15:50', '17:01', 950, 1021, 71],\n",
-    "    [134, '16:03', '16:50', 963, 1010, 47],\n",
-    "    [135, '16:18', '17:44', 978, 1064, 86],\n",
-    "    [136, '16:24', '17:05', 984, 1025, 41],\n",
-    "    [137, '16:28', '17:15', 988, 1035, 47],\n",
-    "    [138, '16:34', '17:15', 994, 1035, 41],\n",
-    "    [139, '16:38', '17:25', 998, 1045, 47],\n",
-    "    [140, '16:40', '16:56', 1000, 1016, 16],\n",
-    "    [141, '16:45', '17:04', 1005, 1024, 19],\n",
-    "    [142, '16:52', '17:36', 1012, 1056, 44],\n",
-    "    [143, '16:58', '17:45', 1018, 1065, 47],\n",
-    "    [144, '17:04', '18:30', 1024, 1110, 86],\n",
-    "    [145, '17:04', '17:45', 1024, 1065, 41],\n",
-    "    [146, '17:09', '18:03', 1029, 1083, 54],\n",
-    "    [147, '17:18', '18:44', 1038, 1124, 86],\n",
-    "    [148, '17:28', '18:15', 1048, 1095, 47],\n",
-    "    [149, '17:29', '18:41', 1049, 1121, 72],\n",
-    "    [150, '17:36', '18:21', 1056, 1101, 45],\n",
-    "    [151, '17:38', '18:25', 1058, 1105, 47],\n",
-    "    [152, '17:40', '17:56', 1060, 1076, 16],\n",
-    "    [153, '17:45', '18:04', 1065, 1084, 19],\n",
-    "    [154, '17:46', '17:58', 1066, 1078, 12],\n",
-    "    [155, '17:48', '18:35', 1068, 1115, 47],\n",
-    "    [156, '17:49', '18:43', 1069, 1123, 54],\n",
-    "    [157, '17:55', '18:14', 1075, 1094, 19],\n",
-    "    [158, '17:58', '18:45', 1078, 1125, 47],\n",
-    "    [159, '18:00', '19:11', 1080, 1151, 71],\n",
-    "    [160, '18:04', '18:45', 1084, 1125, 41],\n",
-    "    [161, '18:09', '19:03', 1089, 1143, 54],\n",
-    "    [162, '18:13', '19:00', 1093, 1140, 47],\n",
-    "    [163, '18:13', '18:40', 1093, 1120, 27],\n",
-    "    [164, '18:19', '19:13', 1099, 1153, 54],\n",
-    "    [165, '18:28', '19:25', 1108, 1165, 57],\n",
-    "    [166, '18:48', '19:28', 1128, 1168, 40],\n",
-    "    [167, '19:03', '19:45', 1143, 1185, 42],\n",
-    "    [168, '19:20', '19:36', 1160, 1176, 16],\n",
-    "    [169, '19:21', '19:31', 1161, 1171, 10],\n",
-    "    [170, '19:25', '20:04', 1165, 1204, 39],\n",
-    "    [171, '19:26', '20:08', 1166, 1208, 42],\n",
-    "    [172, '19:30', '19:40', 1170, 1180, 10],\n",
-    "    [173, '19:44', '20:33', 1184, 1233, 49],\n",
-    "    [174, '19:48', '21:09', 1188, 1269, 81],\n",
-    "    [175, '19:53', '21:02', 1193, 1262, 69],\n",
-    "    [176, '20:04', '20:29', 1204, 1229, 25],\n",
-    "    [177, '20:17', '21:03', 1217, 1263, 46],\n",
-    "    [178, '20:20', '20:57', 1220, 1257, 37],\n",
-    "    [179, '20:29', '21:18', 1229, 1278, 49],\n",
-    "    [180, '20:35', '21:54', 1235, 1314, 79],\n",
-    "    [181, '20:40', '20:50', 1240, 1250, 10],\n",
-    "    [182, '20:47', '21:42', 1247, 1302, 55],\n",
-    "    [183, '21:00', '21:10', 1260, 1270, 10],\n",
-    "    [184, '21:07', '21:44', 1267, 1304, 37],\n",
-    "    [185, '21:14', '22:03', 1274, 1323, 49],\n",
-    "    [186, '21:39', '21:55', 1299, 1315, 16],\n",
-    "    [187, '21:40', '22:17', 1300, 1337, 37],\n",
-    "    [188, '21:40', '21:50', 1300, 1310, 10],\n",
-    "    [189, '21:48', '22:03', 1308, 1323, 15],\n",
-    "    [190, '22:17', '23:03', 1337, 1383, 46],\n",
-    "    [191, '22:43', '23:08', 1363, 1388, 25],\n",
-    "    [192, '23:35', '01:05', 1415, 1505, 90],\n",
-    "    [193, '23:46', '00:01', 1426, 1441, 15],\n",
-    "    [194, '23:47', '00:33', 1427, 1473, 46],\n",
-    "    [195, '23:52', '00:24', 1432, 1464, 32],\n",
-    "    [196, '23:58', '00:38', 1438, 1478, 40],\n",
-    "    [197, '00:02', '00:12', 1442, 1452, 10],\n",
-    "    [198, '00:07', '00:39', 1447, 1479, 32],\n",
-    "    [199, '00:25', '01:12', 1465, 1512, 47]\n",
+    "    [0, \"04:30\", \"04:53\", 270, 293, 23],\n",
+    "    [1, \"04:46\", \"04:56\", 286, 296, 10],\n",
+    "    [2, \"04:52\", \"05:56\", 292, 356, 64],\n",
+    "    [3, \"04:53\", \"05:23\", 293, 323, 30],\n",
+    "    [4, \"05:07\", \"05:44\", 307, 344, 37],\n",
+    "    [5, \"05:10\", \"06:06\", 310, 366, 56],\n",
+    "    [6, \"05:18\", \"06:03\", 318, 363, 45],\n",
+    "    [7, \"05:30\", \"05:40\", 330, 340, 10],\n",
+    "    [8, \"05:30\", \"05:40\", 330, 340, 10],\n",
+    "    [9, \"05:33\", \"06:15\", 333, 375, 42],\n",
+    "    [10, \"05:40\", \"05:50\", 340, 350, 10],\n",
+    "    [11, \"05:43\", \"06:08\", 343, 368, 25],\n",
+    "    [12, \"05:54\", \"07:20\", 354, 440, 86],\n",
+    "    [13, \"06:04\", \"06:37\", 364, 397, 33],\n",
+    "    [14, \"06:13\", \"06:58\", 373, 418, 45],\n",
+    "    [15, \"06:14\", \"07:40\", 374, 460, 86],\n",
+    "    [16, \"06:15\", \"07:15\", 375, 435, 60],\n",
+    "    [17, \"06:16\", \"06:26\", 376, 386, 10],\n",
+    "    [18, \"06:17\", \"06:34\", 377, 394, 17],\n",
+    "    [19, \"06:20\", \"06:36\", 380, 396, 16],\n",
+    "    [20, \"06:22\", \"07:06\", 382, 426, 44],\n",
+    "    [21, \"06:24\", \"07:50\", 384, 470, 86],\n",
+    "    [22, \"06:27\", \"06:44\", 387, 404, 17],\n",
+    "    [23, \"06:30\", \"06:40\", 390, 400, 10],\n",
+    "    [24, \"06:31\", \"06:43\", 391, 403, 12],\n",
+    "    [25, \"06:33\", \"07:53\", 393, 473, 80],\n",
+    "    [26, \"06:34\", \"07:09\", 394, 429, 35],\n",
+    "    [27, \"06:40\", \"06:56\", 400, 416, 16],\n",
+    "    [28, \"06:44\", \"07:17\", 404, 437, 33],\n",
+    "    [29, \"06:46\", \"06:58\", 406, 418, 12],\n",
+    "    [30, \"06:49\", \"07:43\", 409, 463, 54],\n",
+    "    [31, \"06:50\", \"07:05\", 410, 425, 15],\n",
+    "    [32, \"06:52\", \"07:36\", 412, 456, 44],\n",
+    "    [33, \"06:54\", \"07:27\", 414, 447, 33],\n",
+    "    [34, \"06:56\", \"08:23\", 416, 503, 87],\n",
+    "    [35, \"07:04\", \"07:44\", 424, 464, 40],\n",
+    "    [36, \"07:11\", \"08:36\", 431, 516, 85],\n",
+    "    [37, \"07:17\", \"07:35\", 437, 455, 18],\n",
+    "    [38, \"07:22\", \"08:06\", 442, 486, 44],\n",
+    "    [39, \"07:27\", \"08:15\", 447, 495, 48],\n",
+    "    [40, \"07:35\", \"07:45\", 455, 465, 10],\n",
+    "    [41, \"07:43\", \"08:08\", 463, 488, 25],\n",
+    "    [42, \"07:50\", \"08:37\", 470, 517, 47],\n",
+    "    [43, \"07:58\", \"08:45\", 478, 525, 47],\n",
+    "    [44, \"08:00\", \"08:35\", 480, 515, 35],\n",
+    "    [45, \"08:06\", \"08:51\", 486, 531, 45],\n",
+    "    [46, \"08:10\", \"08:45\", 490, 525, 35],\n",
+    "    [47, \"08:15\", \"08:30\", 495, 510, 15],\n",
+    "    [48, \"08:16\", \"09:00\", 496, 540, 44],\n",
+    "    [49, \"08:18\", \"09:16\", 498, 556, 58],\n",
+    "    [50, \"08:20\", \"08:36\", 500, 516, 16],\n",
+    "    [51, \"08:27\", \"09:07\", 507, 547, 40],\n",
+    "    [52, \"08:30\", \"08:45\", 510, 525, 15],\n",
+    "    [53, \"08:35\", \"09:15\", 515, 555, 40],\n",
+    "    [54, \"08:46\", \"09:30\", 526, 570, 44],\n",
+    "    [55, \"08:51\", \"09:17\", 531, 557, 26],\n",
+    "    [56, \"08:55\", \"09:15\", 535, 555, 20],\n",
+    "    [57, \"08:58\", \"09:38\", 538, 578, 40],\n",
+    "    [58, \"09:00\", \"09:35\", 540, 575, 35],\n",
+    "    [59, \"09:00\", \"09:16\", 540, 556, 16],\n",
+    "    [60, \"09:20\", \"09:36\", 560, 576, 16],\n",
+    "    [61, \"09:31\", \"09:43\", 571, 583, 12],\n",
+    "    [62, \"09:33\", \"10:15\", 573, 615, 42],\n",
+    "    [63, \"09:54\", \"10:05\", 594, 605, 11],\n",
+    "    [64, \"10:11\", \"10:38\", 611, 638, 27],\n",
+    "    [65, \"10:18\", \"11:00\", 618, 660, 42],\n",
+    "    [66, \"10:21\", \"10:47\", 621, 647, 26],\n",
+    "    [67, \"10:25\", \"11:04\", 625, 664, 39],\n",
+    "    [68, \"10:26\", \"11:08\", 626, 668, 42],\n",
+    "    [69, \"10:44\", \"12:11\", 644, 731, 87],\n",
+    "    [70, \"11:00\", \"11:16\", 660, 676, 16],\n",
+    "    [71, \"11:15\", \"11:54\", 675, 714, 39],\n",
+    "    [72, \"11:16\", \"11:28\", 676, 688, 12],\n",
+    "    [73, \"11:20\", \"11:30\", 680, 690, 10],\n",
+    "    [74, \"11:21\", \"11:47\", 681, 707, 26],\n",
+    "    [75, \"11:25\", \"12:04\", 685, 724, 39],\n",
+    "    [76, \"11:34\", \"11:45\", 694, 705, 11],\n",
+    "    [77, \"11:35\", \"12:14\", 695, 734, 39],\n",
+    "    [78, \"11:41\", \"12:23\", 701, 743, 42],\n",
+    "    [79, \"11:44\", \"12:35\", 704, 755, 51],\n",
+    "    [80, \"11:46\", \"11:58\", 706, 718, 12],\n",
+    "    [81, \"12:00\", \"12:10\", 720, 730, 10],\n",
+    "    [82, \"12:04\", \"12:15\", 724, 735, 11],\n",
+    "    [83, \"12:04\", \"13:04\", 724, 784, 60],\n",
+    "    [84, \"12:11\", \"12:38\", 731, 758, 27],\n",
+    "    [85, \"12:15\", \"12:54\", 735, 774, 39],\n",
+    "    [86, \"12:25\", \"13:10\", 745, 790, 45],\n",
+    "    [87, \"12:30\", \"12:40\", 750, 760, 10],\n",
+    "    [88, \"12:34\", \"13:58\", 754, 838, 84],\n",
+    "    [89, \"12:38\", \"13:25\", 758, 805, 47],\n",
+    "    [90, \"12:48\", \"13:35\", 768, 815, 47],\n",
+    "    [91, \"13:00\", \"13:16\", 780, 796, 16],\n",
+    "    [92, \"13:05\", \"13:44\", 785, 824, 39],\n",
+    "    [93, \"13:08\", \"13:55\", 788, 835, 47],\n",
+    "    [94, \"13:14\", \"14:38\", 794, 878, 84],\n",
+    "    [95, \"13:23\", \"13:49\", 803, 829, 26],\n",
+    "    [96, \"13:25\", \"14:04\", 805, 844, 39],\n",
+    "    [97, \"13:28\", \"14:54\", 808, 894, 86],\n",
+    "    [98, \"13:31\", \"13:43\", 811, 823, 12],\n",
+    "    [99, \"13:34\", \"14:58\", 814, 898, 84],\n",
+    "    [100, \"13:38\", \"14:25\", 818, 865, 47],\n",
+    "    [101, \"13:38\", \"15:04\", 818, 904, 86],\n",
+    "    [102, \"13:39\", \"14:33\", 819, 873, 54],\n",
+    "    [103, \"13:40\", \"13:50\", 820, 830, 10],\n",
+    "    [104, \"13:43\", \"14:10\", 823, 850, 27],\n",
+    "    [105, \"13:48\", \"14:35\", 828, 875, 47],\n",
+    "    [106, \"13:48\", \"14:35\", 828, 875, 47],\n",
+    "    [107, \"13:53\", \"14:40\", 833, 880, 47],\n",
+    "    [108, \"13:58\", \"15:24\", 838, 924, 86],\n",
+    "    [109, \"13:58\", \"14:25\", 838, 865, 27],\n",
+    "    [110, \"14:00\", \"14:16\", 840, 856, 16],\n",
+    "    [111, \"14:13\", \"15:00\", 853, 900, 47],\n",
+    "    [112, \"14:20\", \"15:31\", 860, 931, 71],\n",
+    "    [113, \"14:25\", \"15:02\", 865, 902, 37],\n",
+    "    [114, \"14:34\", \"14:45\", 874, 885, 11],\n",
+    "    [115, \"14:40\", \"15:51\", 880, 951, 71],\n",
+    "    [116, \"14:40\", \"14:56\", 880, 896, 16],\n",
+    "    [117, \"14:46\", \"14:58\", 886, 898, 12],\n",
+    "    [118, \"14:49\", \"15:43\", 889, 943, 54],\n",
+    "    [119, \"14:52\", \"15:21\", 892, 921, 29],\n",
+    "    [120, \"14:58\", \"16:24\", 898, 984, 86],\n",
+    "    [121, \"14:59\", \"15:53\", 899, 953, 54],\n",
+    "    [122, \"15:00\", \"15:10\", 900, 910, 10],\n",
+    "    [123, \"15:00\", \"15:35\", 900, 935, 35],\n",
+    "    [124, \"15:08\", \"15:45\", 908, 945, 37],\n",
+    "    [125, \"15:12\", \"15:36\", 912, 936, 24],\n",
+    "    [126, \"15:18\", \"16:05\", 918, 965, 47],\n",
+    "    [127, \"15:24\", \"16:05\", 924, 965, 41],\n",
+    "    [128, \"15:31\", \"15:43\", 931, 943, 12],\n",
+    "    [129, \"15:35\", \"15:54\", 935, 954, 19],\n",
+    "    [130, \"15:36\", \"16:21\", 936, 981, 45],\n",
+    "    [131, \"15:39\", \"16:33\", 939, 993, 54],\n",
+    "    [132, \"15:48\", \"16:35\", 948, 995, 47],\n",
+    "    [133, \"15:50\", \"17:01\", 950, 1021, 71],\n",
+    "    [134, \"16:03\", \"16:50\", 963, 1010, 47],\n",
+    "    [135, \"16:18\", \"17:44\", 978, 1064, 86],\n",
+    "    [136, \"16:24\", \"17:05\", 984, 1025, 41],\n",
+    "    [137, \"16:28\", \"17:15\", 988, 1035, 47],\n",
+    "    [138, \"16:34\", \"17:15\", 994, 1035, 41],\n",
+    "    [139, \"16:38\", \"17:25\", 998, 1045, 47],\n",
+    "    [140, \"16:40\", \"16:56\", 1000, 1016, 16],\n",
+    "    [141, \"16:45\", \"17:04\", 1005, 1024, 19],\n",
+    "    [142, \"16:52\", \"17:36\", 1012, 1056, 44],\n",
+    "    [143, \"16:58\", \"17:45\", 1018, 1065, 47],\n",
+    "    [144, \"17:04\", \"18:30\", 1024, 1110, 86],\n",
+    "    [145, \"17:04\", \"17:45\", 1024, 1065, 41],\n",
+    "    [146, \"17:09\", \"18:03\", 1029, 1083, 54],\n",
+    "    [147, \"17:18\", \"18:44\", 1038, 1124, 86],\n",
+    "    [148, \"17:28\", \"18:15\", 1048, 1095, 47],\n",
+    "    [149, \"17:29\", \"18:41\", 1049, 1121, 72],\n",
+    "    [150, \"17:36\", \"18:21\", 1056, 1101, 45],\n",
+    "    [151, \"17:38\", \"18:25\", 1058, 1105, 47],\n",
+    "    [152, \"17:40\", \"17:56\", 1060, 1076, 16],\n",
+    "    [153, \"17:45\", \"18:04\", 1065, 1084, 19],\n",
+    "    [154, \"17:46\", \"17:58\", 1066, 1078, 12],\n",
+    "    [155, \"17:48\", \"18:35\", 1068, 1115, 47],\n",
+    "    [156, \"17:49\", \"18:43\", 1069, 1123, 54],\n",
+    "    [157, \"17:55\", \"18:14\", 1075, 1094, 19],\n",
+    "    [158, \"17:58\", \"18:45\", 1078, 1125, 47],\n",
+    "    [159, \"18:00\", \"19:11\", 1080, 1151, 71],\n",
+    "    [160, \"18:04\", \"18:45\", 1084, 1125, 41],\n",
+    "    [161, \"18:09\", \"19:03\", 1089, 1143, 54],\n",
+    "    [162, \"18:13\", \"19:00\", 1093, 1140, 47],\n",
+    "    [163, \"18:13\", \"18:40\", 1093, 1120, 27],\n",
+    "    [164, \"18:19\", \"19:13\", 1099, 1153, 54],\n",
+    "    [165, \"18:28\", \"19:25\", 1108, 1165, 57],\n",
+    "    [166, \"18:48\", \"19:28\", 1128, 1168, 40],\n",
+    "    [167, \"19:03\", \"19:45\", 1143, 1185, 42],\n",
+    "    [168, \"19:20\", \"19:36\", 1160, 1176, 16],\n",
+    "    [169, \"19:21\", \"19:31\", 1161, 1171, 10],\n",
+    "    [170, \"19:25\", \"20:04\", 1165, 1204, 39],\n",
+    "    [171, \"19:26\", \"20:08\", 1166, 1208, 42],\n",
+    "    [172, \"19:30\", \"19:40\", 1170, 1180, 10],\n",
+    "    [173, \"19:44\", \"20:33\", 1184, 1233, 49],\n",
+    "    [174, \"19:48\", \"21:09\", 1188, 1269, 81],\n",
+    "    [175, \"19:53\", \"21:02\", 1193, 1262, 69],\n",
+    "    [176, \"20:04\", \"20:29\", 1204, 1229, 25],\n",
+    "    [177, \"20:17\", \"21:03\", 1217, 1263, 46],\n",
+    "    [178, \"20:20\", \"20:57\", 1220, 1257, 37],\n",
+    "    [179, \"20:29\", \"21:18\", 1229, 1278, 49],\n",
+    "    [180, \"20:35\", \"21:54\", 1235, 1314, 79],\n",
+    "    [181, \"20:40\", \"20:50\", 1240, 1250, 10],\n",
+    "    [182, \"20:47\", \"21:42\", 1247, 1302, 55],\n",
+    "    [183, \"21:00\", \"21:10\", 1260, 1270, 10],\n",
+    "    [184, \"21:07\", \"21:44\", 1267, 1304, 37],\n",
+    "    [185, \"21:14\", \"22:03\", 1274, 1323, 49],\n",
+    "    [186, \"21:39\", \"21:55\", 1299, 1315, 16],\n",
+    "    [187, \"21:40\", \"22:17\", 1300, 1337, 37],\n",
+    "    [188, \"21:40\", \"21:50\", 1300, 1310, 10],\n",
+    "    [189, \"21:48\", \"22:03\", 1308, 1323, 15],\n",
+    "    [190, \"22:17\", \"23:03\", 1337, 1383, 46],\n",
+    "    [191, \"22:43\", \"23:08\", 1363, 1388, 25],\n",
+    "    [192, \"23:35\", \"01:05\", 1415, 1505, 90],\n",
+    "    [193, \"23:46\", \"00:01\", 1426, 1441, 15],\n",
+    "    [194, \"23:47\", \"00:33\", 1427, 1473, 46],\n",
+    "    [195, \"23:52\", \"00:24\", 1432, 1464, 32],\n",
+    "    [196, \"23:58\", \"00:38\", 1438, 1478, 40],\n",
+    "    [197, \"00:02\", \"00:12\", 1442, 1452, 10],\n",
+    "    [198, \"00:07\", \"00:39\", 1447, 1479, 32],\n",
+    "    [199, \"00:25\", \"01:12\", 1465, 1512, 47],\n",
     "]  # yapf:disable\n",
     "\n",
     "SAMPLE_SHIFTS_LARGE = [\n",
-    "    [0, '04:18', '05:00', 258, 300, 42],\n",
-    "    [1, '04:27', '05:08', 267, 308, 41],\n",
-    "    [2, '04:29', '05:26', 269, 326, 57],\n",
-    "    [3, '04:29', '04:55', 269, 295, 26],\n",
-    "    [4, '04:30', '04:53', 270, 293, 23],\n",
-    "    [5, '04:30', '04:51', 270, 291, 21],\n",
-    "    [6, '04:31', '04:53', 271, 293, 22],\n",
-    "    [7, '04:33', '05:15', 273, 315, 42],\n",
-    "    [8, '04:34', '04:44', 274, 284, 10],\n",
-    "    [9, '04:34', '05:03', 274, 303, 29],\n",
-    "    [10, '04:35', '04:50', 275, 290, 15],\n",
-    "    [11, '04:36', '04:46', 276, 286, 10],\n",
-    "    [12, '04:37', '05:18', 277, 318, 41],\n",
-    "    [13, '04:41', '05:13', 281, 313, 32],\n",
-    "    [14, '04:42', '05:23', 282, 323, 41],\n",
-    "    [15, '04:43', '04:53', 283, 293, 10],\n",
-    "    [16, '04:44', '05:45', 284, 345, 61],\n",
-    "    [17, '04:45', '05:11', 285, 311, 26],\n",
-    "    [18, '04:46', '05:01', 286, 301, 15],\n",
-    "    [19, '04:46', '04:56', 286, 296, 10],\n",
-    "    [20, '04:47', '05:14', 287, 314, 27],\n",
-    "    [21, '04:48', '05:30', 288, 330, 42],\n",
-    "    [22, '04:49', '05:41', 289, 341, 52],\n",
-    "    [23, '04:49', '05:18', 289, 318, 29],\n",
-    "    [24, '04:50', '05:33', 290, 333, 43],\n",
-    "    [25, '04:52', '05:56', 292, 356, 64],\n",
-    "    [26, '04:52', '05:07', 292, 307, 15],\n",
-    "    [27, '04:53', '05:19', 293, 319, 26],\n",
-    "    [28, '04:53', '05:23', 293, 323, 30],\n",
-    "    [29, '04:55', '05:27', 295, 327, 32],\n",
-    "    [30, '04:57', '05:38', 297, 338, 41],\n",
-    "    [31, '05:00', '06:00', 300, 360, 60],\n",
-    "    [32, '05:00', '05:54', 300, 354, 54],\n",
-    "    [33, '05:01', '05:33', 301, 333, 32],\n",
-    "    [34, '05:01', '05:26', 301, 326, 25],\n",
-    "    [35, '05:02', '05:29', 302, 329, 27],\n",
-    "    [36, '05:02', '05:12', 302, 312, 10],\n",
-    "    [37, '05:03', '05:45', 303, 345, 42],\n",
-    "    [38, '05:03', '05:18', 303, 318, 15],\n",
-    "    [39, '05:03', '06:28', 303, 388, 85],\n",
-    "    [40, '05:03', '05:13', 303, 313, 10],\n",
-    "    [41, '05:04', '06:24', 304, 384, 80],\n",
-    "    [42, '05:07', '05:44', 307, 344, 37],\n",
-    "    [43, '05:08', '05:48', 308, 348, 40],\n",
-    "    [44, '05:10', '06:06', 310, 366, 56],\n",
-    "    [45, '05:11', '05:37', 311, 337, 26],\n",
-    "    [46, '05:11', '05:53', 311, 353, 42],\n",
-    "    [47, '05:13', '06:15', 313, 375, 62],\n",
-    "    [48, '05:13', '05:38', 313, 338, 25],\n",
-    "    [49, '05:16', '05:44', 316, 344, 28],\n",
-    "    [50, '05:17', '05:27', 317, 327, 10],\n",
-    "    [51, '05:18', '06:40', 318, 400, 82],\n",
-    "    [52, '05:18', '06:03', 318, 363, 45],\n",
-    "    [53, '05:18', '06:11', 318, 371, 53],\n",
-    "    [54, '05:18', '06:00', 318, 360, 42],\n",
-    "    [55, '05:19', '06:34', 319, 394, 75],\n",
-    "    [56, '05:20', '06:17', 320, 377, 57],\n",
-    "    [57, '05:22', '05:59', 322, 359, 37],\n",
-    "    [58, '05:24', '05:48', 324, 348, 24],\n",
-    "    [59, '05:25', '05:40', 325, 340, 15],\n",
-    "    [60, '05:26', '06:08', 326, 368, 42],\n",
-    "    [61, '05:27', '06:30', 327, 390, 63],\n",
-    "    [62, '05:27', '05:54', 327, 354, 27],\n",
-    "    [63, '05:28', '05:53', 328, 353, 25],\n",
-    "    [64, '05:29', '05:44', 329, 344, 15],\n",
-    "    [65, '05:30', '05:40', 330, 340, 10],\n",
-    "    [66, '05:30', '05:40', 330, 340, 10],\n",
-    "    [67, '05:30', '05:40', 330, 340, 10],\n",
-    "    [68, '05:32', '06:53', 332, 413, 81],\n",
-    "    [69, '05:33', '07:00', 333, 420, 87],\n",
-    "    [70, '05:33', '06:15', 333, 375, 42],\n",
-    "    [71, '05:33', '05:47', 333, 347, 14],\n",
-    "    [72, '05:37', '06:13', 337, 373, 36],\n",
-    "    [73, '05:37', '06:05', 337, 365, 28],\n",
-    "    [74, '05:38', '06:33', 338, 393, 55],\n",
-    "    [75, '05:38', '06:04', 338, 364, 26],\n",
-    "    [76, '05:38', '06:18', 338, 378, 40],\n",
-    "    [77, '05:39', '05:54', 339, 354, 15],\n",
-    "    [78, '05:40', '05:56', 340, 356, 16],\n",
-    "    [79, '05:40', '06:41', 340, 401, 61],\n",
-    "    [80, '05:40', '05:50', 340, 350, 10],\n",
-    "    [81, '05:41', '06:23', 341, 383, 42],\n",
-    "    [82, '05:41', '06:01', 341, 361, 20],\n",
-    "    [83, '05:43', '06:08', 343, 368, 25],\n",
-    "    [84, '05:44', '07:10', 344, 430, 86],\n",
-    "    [85, '05:44', '05:55', 344, 355, 11],\n",
-    "    [86, '05:45', '06:44', 345, 404, 59],\n",
-    "    [87, '05:47', '06:17', 347, 377, 30],\n",
-    "    [88, '05:48', '07:08', 348, 428, 80],\n",
-    "    [89, '05:48', '06:30', 348, 390, 42],\n",
-    "    [90, '05:50', '06:50', 350, 410, 60],\n",
-    "    [91, '05:50', '06:00', 350, 360, 10],\n",
-    "    [92, '05:50', '06:00', 350, 360, 10],\n",
-    "    [93, '05:50', '06:51', 350, 411, 61],\n",
-    "    [94, '05:52', '06:33', 352, 393, 41],\n",
-    "    [95, '05:52', '06:36', 352, 396, 44],\n",
-    "    [96, '05:52', '06:23', 352, 383, 31],\n",
-    "    [97, '05:54', '06:14', 354, 374, 20],\n",
-    "    [98, '05:54', '07:20', 354, 440, 86],\n",
-    "    [99, '05:55', '06:40', 355, 400, 45],\n",
-    "    [100, '05:55', '06:27', 355, 387, 32],\n",
-    "    [101, '05:56', '06:35', 356, 395, 39],\n",
-    "    [102, '05:56', '06:06', 356, 366, 10],\n",
-    "    [103, '05:57', '06:21', 357, 381, 24],\n",
-    "    [104, '05:58', '07:23', 358, 443, 85],\n",
-    "    [105, '05:58', '06:23', 358, 383, 25],\n",
-    "    [106, '05:58', '06:08', 358, 368, 10],\n",
-    "    [107, '05:58', '06:43', 358, 403, 45],\n",
-    "    [108, '06:00', '06:10', 360, 370, 10],\n",
-    "    [109, '06:00', '06:16', 360, 376, 16],\n",
-    "    [110, '06:00', '07:01', 360, 421, 61],\n",
-    "    [111, '06:01', '07:00', 361, 420, 59],\n",
-    "    [112, '06:01', '06:13', 361, 373, 12],\n",
-    "    [113, '06:01', '06:45', 361, 405, 44],\n",
-    "    [114, '06:03', '06:50', 363, 410, 47],\n",
-    "    [115, '06:04', '06:37', 364, 397, 33],\n",
-    "    [116, '06:04', '07:30', 364, 450, 86],\n",
-    "    [117, '06:05', '06:24', 365, 384, 19],\n",
-    "    [118, '06:06', '06:51', 366, 411, 45],\n",
-    "    [119, '06:07', '06:43', 367, 403, 36],\n",
-    "    [120, '06:08', '07:30', 368, 450, 82],\n",
-    "    [121, '06:10', '06:20', 370, 380, 10],\n",
-    "    [122, '06:10', '07:17', 370, 437, 67],\n",
-    "    [123, '06:11', '06:54', 371, 414, 43],\n",
-    "    [124, '06:11', '06:21', 371, 381, 10],\n",
-    "    [125, '06:13', '06:38', 373, 398, 25],\n",
-    "    [126, '06:13', '06:58', 373, 418, 45],\n",
-    "    [127, '06:13', '06:53', 373, 413, 40],\n",
-    "    [128, '06:14', '07:03', 374, 423, 49],\n",
-    "    [129, '06:14', '06:47', 374, 407, 33],\n",
-    "    [130, '06:14', '07:40', 374, 460, 86],\n",
-    "    [131, '06:15', '07:15', 375, 435, 60],\n",
-    "    [132, '06:16', '06:28', 376, 388, 12],\n",
-    "    [133, '06:16', '06:26', 376, 386, 10],\n",
-    "    [134, '06:17', '06:34', 377, 394, 17],\n",
-    "    [135, '06:18', '07:06', 378, 426, 48],\n",
-    "    [136, '06:18', '07:38', 378, 458, 80],\n",
-    "    [137, '06:18', '07:02', 378, 422, 44],\n",
-    "    [138, '06:19', '06:53', 379, 413, 34],\n",
-    "    [139, '06:20', '07:25', 380, 445, 65],\n",
-    "    [140, '06:20', '06:36', 380, 396, 16],\n",
-    "    [141, '06:20', '06:30', 380, 390, 10],\n",
-    "    [142, '06:20', '06:30', 380, 390, 10],\n",
-    "    [143, '06:21', '06:49', 381, 409, 28],\n",
-    "    [144, '06:22', '07:06', 382, 426, 44],\n",
-    "    [145, '06:24', '07:50', 384, 470, 86],\n",
-    "    [146, '06:24', '06:57', 384, 417, 33],\n",
-    "    [147, '06:26', '07:45', 386, 465, 79],\n",
-    "    [148, '06:26', '07:10', 386, 430, 44],\n",
-    "    [149, '06:27', '06:44', 387, 404, 17],\n",
-    "    [150, '06:28', '06:53', 388, 413, 25],\n",
-    "    [151, '06:28', '07:14', 388, 434, 46],\n",
-    "    [152, '06:29', '07:03', 389, 423, 34],\n",
-    "    [153, '06:30', '06:40', 390, 400, 10],\n",
-    "    [154, '06:30', '07:37', 390, 457, 67],\n",
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+    "    [1346, \"00:24\", \"00:40\", 1464, 1480, 16],\n",
+    "    [1347, \"00:25\", \"01:12\", 1465, 1512, 47],\n",
+    "    [1348, \"00:28\", \"01:07\", 1468, 1507, 39],\n",
+    "    [1349, \"00:33\", \"01:05\", 1473, 1505, 32],\n",
+    "    [1350, \"00:43\", \"01:21\", 1483, 1521, 38],\n",
+    "    [1351, \"00:44\", \"00:54\", 1484, 1494, 10],\n",
+    "    [1352, \"00:47\", \"01:09\", 1487, 1509, 22],\n",
+    "    [1353, \"00:47\", \"01:26\", 1487, 1526, 39],\n",
+    "    [1354, \"00:54\", \"01:04\", 1494, 1504, 10],\n",
+    "    [1355, \"00:57\", \"01:07\", 1497, 1507, 10],\n",
     "]  # yapf:disable\n",
     "\n",
     "\n",
-    "\n",
     "def bus_driver_scheduling(minimize_drivers, max_num_drivers):\n",
     "    \"\"\"Optimize the bus driver scheduling problem.\n",
     "\n",
-    "  This model has two modes.\n",
+    "    This model has two modes.\n",
     "\n",
-    "  If minimize_drivers == True, the objective will be to find the minimal\n",
-    "  number of drivers, independently of the working times of each drivers.\n",
+    "    If minimize_drivers == True, the objective will be to find the minimal\n",
+    "    number of drivers, independently of the working times of each drivers.\n",
     "\n",
-    "  Otherwise, will will create max_num_drivers non optional drivers, and\n",
-    "  minimize the sum of working times of these drivers.\n",
+    "    Otherwise, will will create max_num_drivers non optional drivers, and\n",
+    "    minimize the sum of working times of these drivers.\n",
     "\n",
-    "  Args:\n",
-    "    minimize_drivers: A Boolean parameter specifying the objective of the\n",
-    "      problem. If True, it tries to minimize the number of used drivers. If\n",
-    "      false, it minimizes the sum of working times per workers.\n",
-    "    max_num_drivers: This number specifies the exact number of non optional\n",
-    "      drivers to use. This is only used if 'minimize_drivers' is False.\n",
+    "    Args:\n",
+    "      minimize_drivers: A Boolean parameter specifying the objective of the\n",
+    "        problem. If True, it tries to minimize the number of used drivers. If\n",
+    "        false, it minimizes the sum of working times per workers.\n",
+    "      max_num_drivers: This number specifies the exact number of non optional\n",
+    "        drivers to use. This is only used if 'minimize_drivers' is False.\n",
     "\n",
-    "  Returns:\n",
-    "      The objective value of the model.\n",
-    "  \"\"\"\n",
+    "    Returns:\n",
+    "        The objective value of the model.\n",
+    "    \"\"\"\n",
     "    shifts = None\n",
     "    if _INSTANCE.value == 0:\n",
     "        shifts = SAMPLE_SHIFTS_TINY\n",
@@ -1817,19 +1820,18 @@
     "\n",
     "    # Computed data.\n",
     "    total_driving_time = sum(shift[5] for shift in shifts)\n",
-    "    min_num_drivers = int(math.ceil(total_driving_time * 1.0 /\n",
-    "                                    max_driving_time))\n",
+    "    min_num_drivers = int(math.ceil(total_driving_time * 1.0 / max_driving_time))\n",
     "    num_drivers = 2 * min_num_drivers if minimize_drivers else max_num_drivers\n",
     "    min_start_time = min(shift[3] for shift in shifts)\n",
     "    max_end_time = max(shift[4] for shift in shifts)\n",
     "\n",
-    "    print('Bus driver scheduling')\n",
-    "    print('  num shifts =', num_shifts)\n",
-    "    print('  total driving time =', total_driving_time, 'minutes')\n",
-    "    print('  min num drivers =', min_num_drivers)\n",
-    "    print('  num drivers =', num_drivers)\n",
-    "    print('  min start time =', min_start_time)\n",
-    "    print('  max end time =', max_end_time)\n",
+    "    print(\"Bus driver scheduling\")\n",
+    "    print(\"  num shifts =\", num_shifts)\n",
+    "    print(\"  total driving time =\", total_driving_time, \"minutes\")\n",
+    "    print(\"  min num drivers =\", min_num_drivers)\n",
+    "    print(\"  num drivers =\", num_drivers)\n",
+    "    print(\"  min start time =\", min_start_time)\n",
+    "    print(\"  max end time =\", max_end_time)\n",
     "\n",
     "    model = cp_model.CpModel()\n",
     "\n",
@@ -1871,15 +1873,15 @@
     "\n",
     "    for d in range(num_drivers):\n",
     "        start_times.append(\n",
-    "            model.NewIntVar(min_start_time - setup_time, max_end_time,\n",
-    "                            'start_%i' % d))\n",
+    "            model.NewIntVar(min_start_time - setup_time, max_end_time, \"start_%i\" % d)\n",
+    "        )\n",
     "        end_times.append(\n",
-    "            model.NewIntVar(min_start_time, max_end_time + cleanup_time,\n",
-    "                            'end_%i' % d))\n",
-    "        driving_times.append(\n",
-    "            model.NewIntVar(0, max_driving_time, 'driving_%i' % d))\n",
+    "            model.NewIntVar(min_start_time, max_end_time + cleanup_time, \"end_%i\" % d)\n",
+    "        )\n",
+    "        driving_times.append(model.NewIntVar(0, max_driving_time, \"driving_%i\" % d))\n",
     "        working_times.append(\n",
-    "            model.NewIntVar(0, max_working_time, 'working_times_%i' % d))\n",
+    "            model.NewIntVar(0, max_working_time, \"working_times_%i\" % d)\n",
+    "        )\n",
     "\n",
     "        incoming_literals = collections.defaultdict(list)\n",
     "        outgoing_literals = collections.defaultdict(list)\n",
@@ -1889,11 +1891,13 @@
     "        # Create all the shift variables before iterating on the transitions\n",
     "        # between these shifts.\n",
     "        for s in range(num_shifts):\n",
-    "            total_driving[d, s] = model.NewIntVar(0, max_driving_time,\n",
-    "                                                  'dr_%i_%i' % (d, s))\n",
+    "            total_driving[d, s] = model.NewIntVar(\n",
+    "                0, max_driving_time, \"dr_%i_%i\" % (d, s)\n",
+    "            )\n",
     "            no_break_driving[d, s] = model.NewIntVar(\n",
-    "                0, max_driving_time_without_pauses, 'mdr_%i_%i' % (d, s))\n",
-    "            performed[d, s] = model.NewBoolVar('performed_%i_%i' % (d, s))\n",
+    "                0, max_driving_time_without_pauses, \"mdr_%i_%i\" % (d, s)\n",
+    "            )\n",
+    "            performed[d, s] = model.NewBoolVar(\"performed_%i_%i\" % (d, s))\n",
     "\n",
     "        for s in range(num_shifts):\n",
     "            shift = shifts[s]\n",
@@ -1902,37 +1906,30 @@
     "            # Arc from source to shift.\n",
     "            #    - set the start time of the driver\n",
     "            #    - increase driving time and driving time since break\n",
-    "            source_lit = model.NewBoolVar('%i from source to %i' % (d, s))\n",
+    "            source_lit = model.NewBoolVar(\"%i from source to %i\" % (d, s))\n",
     "            outgoing_source_literals.append(source_lit)\n",
     "            incoming_literals[s].append(source_lit)\n",
     "            shared_incoming_literals[s].append(source_lit)\n",
-    "            model.Add(start_times[d] == shift[3] -\n",
-    "                      setup_time).OnlyEnforceIf(source_lit)\n",
+    "            model.Add(start_times[d] == shift[3] - setup_time).OnlyEnforceIf(source_lit)\n",
     "            model.Add(total_driving[d, s] == duration).OnlyEnforceIf(source_lit)\n",
-    "            model.Add(no_break_driving[d,\n",
-    "                                       s] == duration).OnlyEnforceIf(source_lit)\n",
+    "            model.Add(no_break_driving[d, s] == duration).OnlyEnforceIf(source_lit)\n",
     "            starting_shifts[d, s] = source_lit\n",
     "\n",
     "            # Arc from shift to sink\n",
     "            #    - set the end time of the driver\n",
     "            #    - set the driving times of the driver\n",
-    "            sink_lit = model.NewBoolVar('%i from %i to sink' % (d, s))\n",
+    "            sink_lit = model.NewBoolVar(\"%i from %i to sink\" % (d, s))\n",
     "            outgoing_literals[s].append(sink_lit)\n",
     "            shared_outgoing_literals[s].append(sink_lit)\n",
     "            incoming_sink_literals.append(sink_lit)\n",
-    "            model.Add(end_times[d] == shift[4] +\n",
-    "                      cleanup_time).OnlyEnforceIf(sink_lit)\n",
-    "            model.Add(\n",
-    "                driving_times[d] == total_driving[d, s]).OnlyEnforceIf(sink_lit)\n",
+    "            model.Add(end_times[d] == shift[4] + cleanup_time).OnlyEnforceIf(sink_lit)\n",
+    "            model.Add(driving_times[d] == total_driving[d, s]).OnlyEnforceIf(sink_lit)\n",
     "\n",
     "            # Node not performed\n",
     "            #    - set both driving times to 0\n",
     "            #    - add a looping arc on the node\n",
-    "            model.Add(total_driving[d,\n",
-    "                                    s] == 0).OnlyEnforceIf(performed[d,\n",
-    "                                                                     s].Not())\n",
-    "            model.Add(no_break_driving[d, s] == 0).OnlyEnforceIf(\n",
-    "                performed[d, s].Not())\n",
+    "            model.Add(total_driving[d, s] == 0).OnlyEnforceIf(performed[d, s].Not())\n",
+    "            model.Add(no_break_driving[d, s] == 0).OnlyEnforceIf(performed[d, s].Not())\n",
     "            incoming_literals[s].append(performed[d, s].Not())\n",
     "            outgoing_literals[s].append(performed[d, s].Not())\n",
     "            # Not adding to the shared lists, because, globally, each node will have\n",
@@ -1942,28 +1939,31 @@
     "            #    - add upper bound on start_time\n",
     "            #    - add lower bound on end_times\n",
     "            model.Add(start_times[d] <= shift[3] - setup_time).OnlyEnforceIf(\n",
-    "                performed[d, s])\n",
+    "                performed[d, s]\n",
+    "            )\n",
     "            model.Add(end_times[d] >= shift[4] + cleanup_time).OnlyEnforceIf(\n",
-    "                performed[d, s])\n",
+    "                performed[d, s]\n",
+    "            )\n",
     "\n",
     "            for o in range(num_shifts):\n",
     "                other = shifts[o]\n",
     "                delay = other[3] - shift[4]\n",
     "                if delay < min_delay_between_shifts:\n",
     "                    continue\n",
-    "                lit = model.NewBoolVar('%i from %i to %i' % (d, s, o))\n",
+    "                lit = model.NewBoolVar(\"%i from %i to %i\" % (d, s, o))\n",
     "\n",
     "                # Increase driving time\n",
-    "                model.Add(total_driving[d, o] == total_driving[d, s] +\n",
-    "                          other[5]).OnlyEnforceIf(lit)\n",
+    "                model.Add(\n",
+    "                    total_driving[d, o] == total_driving[d, s] + other[5]\n",
+    "                ).OnlyEnforceIf(lit)\n",
     "\n",
     "                # Increase no_break_driving or reset it to 0 depending on the delay\n",
     "                if delay >= min_pause_after_4h:\n",
-    "                    model.Add(\n",
-    "                        no_break_driving[d, o] == other[5]).OnlyEnforceIf(lit)\n",
+    "                    model.Add(no_break_driving[d, o] == other[5]).OnlyEnforceIf(lit)\n",
     "                else:\n",
-    "                    model.Add(no_break_driving[d, o] == no_break_driving[d, s] +\n",
-    "                              other[5]).OnlyEnforceIf(lit)\n",
+    "                    model.Add(\n",
+    "                        no_break_driving[d, o] == no_break_driving[d, s] + other[5]\n",
+    "                    ).OnlyEnforceIf(lit)\n",
     "\n",
     "                # Add arc\n",
     "                outgoing_literals[s].append(lit)\n",
@@ -1979,18 +1979,15 @@
     "\n",
     "        if minimize_drivers:\n",
     "            # Driver is not working.\n",
-    "            working = model.NewBoolVar('working_%i' % d)\n",
-    "            model.Add(start_times[d] == min_start_time).OnlyEnforceIf(\n",
-    "                working.Not())\n",
-    "            model.Add(end_times[d] == min_start_time).OnlyEnforceIf(\n",
-    "                working.Not())\n",
+    "            working = model.NewBoolVar(\"working_%i\" % d)\n",
+    "            model.Add(start_times[d] == min_start_time).OnlyEnforceIf(working.Not())\n",
+    "            model.Add(end_times[d] == min_start_time).OnlyEnforceIf(working.Not())\n",
     "            model.Add(driving_times[d] == 0).OnlyEnforceIf(working.Not())\n",
     "            working_drivers.append(working)\n",
     "            outgoing_source_literals.append(working.Not())\n",
     "            incoming_sink_literals.append(working.Not())\n",
     "            # Conditional working time constraints\n",
-    "            model.Add(\n",
-    "                working_times[d] >= min_working_time).OnlyEnforceIf(working)\n",
+    "            model.Add(working_times[d] >= min_working_time).OnlyEnforceIf(working)\n",
     "            model.Add(working_times[d] == 0).OnlyEnforceIf(working.Not())\n",
     "        else:\n",
     "            # Working time constraints\n",
@@ -2021,16 +2018,17 @@
     "    if minimize_drivers:\n",
     "        # Push non working drivers to the end\n",
     "        for d in range(num_drivers - 1):\n",
-    "            model.AddImplication(working_drivers[d].Not(),\n",
-    "                                 working_drivers[d + 1].Not())\n",
+    "            model.AddImplication(working_drivers[d].Not(), working_drivers[d + 1].Not())\n",
     "\n",
     "    # Redundant constraints: sum of driving times = sum of shift driving times\n",
     "    model.Add(cp_model.LinearExpr.Sum(driving_times) == total_driving_time)\n",
     "    if not minimize_drivers:\n",
     "        model.Add(\n",
-    "            cp_model.LinearExpr.Sum(working_times) == total_driving_time +\n",
-    "            num_drivers * (setup_time + cleanup_time) +\n",
-    "            cp_model.LinearExpr.WeightedSum(delay_literals, delay_weights))\n",
+    "            cp_model.LinearExpr.Sum(working_times)\n",
+    "            == total_driving_time\n",
+    "            + num_drivers * (setup_time + cleanup_time)\n",
+    "            + cp_model.LinearExpr.WeightedSum(delay_literals, delay_weights)\n",
+    "        )\n",
     "\n",
     "    if minimize_drivers:\n",
     "        # Minimize the number of working drivers\n",
@@ -2038,12 +2036,11 @@
     "    else:\n",
     "        # Minimize the sum of delays between tasks, which in turns minimize the\n",
     "        # sum of working times as the total driving time is fixed\n",
-    "        model.Minimize(\n",
-    "            cp_model.LinearExpr.WeightedSum(delay_literals, delay_weights))\n",
+    "        model.Minimize(cp_model.LinearExpr.WeightedSum(delay_literals, delay_weights))\n",
     "\n",
     "    if not minimize_drivers and _OUTPUT_PROTO.value:\n",
-    "        print('Writing proto to %s' % _OUTPUT_PROTO.value)\n",
-    "        with open(_OUTPUT_PROTO.value, 'w') as text_file:\n",
+    "        print(\"Writing proto to %s\" % _OUTPUT_PROTO.value)\n",
+    "        with open(_OUTPUT_PROTO.value, \"w\") as text_file:\n",
     "            text_file.write(str(model))\n",
     "\n",
     "    # Solve model.\n",
@@ -2059,14 +2056,16 @@
     "    # Display solution\n",
     "    if minimize_drivers:\n",
     "        max_num_drivers = int(solver.ObjectiveValue())\n",
-    "        print('minimal number of drivers =', max_num_drivers)\n",
+    "        print(\"minimal number of drivers =\", max_num_drivers)\n",
     "        return max_num_drivers\n",
     "\n",
     "    for d in range(num_drivers):\n",
-    "        print('Driver %i: ' % (d + 1))\n",
-    "        print('  total driving time =', solver.Value(driving_times[d]))\n",
-    "        print('  working time =',\n",
-    "              solver.Value(working_times[d]) + setup_time + cleanup_time)\n",
+    "        print(\"Driver %i: \" % (d + 1))\n",
+    "        print(\"  total driving time =\", solver.Value(driving_times[d]))\n",
+    "        print(\n",
+    "            \"  working time =\",\n",
+    "            solver.Value(working_times[d]) + setup_time + cleanup_time,\n",
+    "        )\n",
     "\n",
     "        first = True\n",
     "        for s in range(num_shifts):\n",
@@ -2079,8 +2078,8 @@
     "            # this one exceeds 30 minutes. For this, we look at the\n",
     "            # no_break_driving which was reinitialized in that case.\n",
     "            if solver.Value(no_break_driving[d, s]) == shift[5] and not first:\n",
-    "                print('    **break**')\n",
-    "            print('    shift ', shift[0], ':', shift[1], '-', shift[2])\n",
+    "                print(\"    **break**\")\n",
+    "            print(\"    shift \", shift[0], \":\", shift[1], \"-\", shift[2])\n",
     "            first = False\n",
     "\n",
     "    return int(solver.ObjectiveValue())\n",
@@ -2088,12 +2087,12 @@
     "\n",
     "def main(_):\n",
     "    \"\"\"Optimize the bus driver allocation in two passes.\"\"\"\n",
-    "    print('----------- first pass: minimize the number of drivers')\n",
+    "    print(\"----------- first pass: minimize the number of drivers\")\n",
     "    num_drivers = bus_driver_scheduling(True, -1)\n",
     "    if num_drivers == -1:\n",
-    "        print('no solution found, skipping the final step')\n",
+    "        print(\"no solution found, skipping the final step\")\n",
     "    else:\n",
-    "        print('----------- second pass: minimize the sum of working times')\n",
+    "        print(\"----------- second pass: minimize the sum of working times\")\n",
     "        bus_driver_scheduling(False, num_drivers)\n",
     "\n",
     "\n",
diff --git a/examples/notebook/examples/chemical_balance_sat.ipynb b/examples/notebook/examples/chemical_balance_sat.ipynb
index a002657175..257da3ef03 100644
--- a/examples/notebook/examples/chemical_balance_sat.ipynb
+++ b/examples/notebook/examples/chemical_balance_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "We are trying to group items in equal sized groups.\n",
     "\n",
     "Each item has a color and a value. We want the sum of values of each group to be\n",
@@ -127,9 +128,13 @@
     "    max_set = [\n",
     "        int(\n",
     "            math.ceil(\n",
-    "                min(max_quantities[q][1] * 1000 / chemical_set[s][q + 1]\n",
+    "                min(\n",
+    "                    max_quantities[q][1] * 1000 / chemical_set[s][q + 1]\n",
     "                    for q in all_products\n",
-    "                    if chemical_set[s][q + 1] != 0)))\n",
+    "                    if chemical_set[s][q + 1] != 0\n",
+    "                )\n",
+    "            )\n",
+    "        )\n",
     "        for s in all_sets\n",
     "    ]\n",
     "\n",
@@ -139,14 +144,13 @@
     "\n",
     "    for p in all_products:\n",
     "        model.Add(\n",
-    "            sum(\n",
-    "                int(chemical_set[s][p + 1] * 10) * set_vars[s]\n",
-    "                for s in all_sets) <= int(max_quantities[p][1] * 10000))\n",
+    "            sum(int(chemical_set[s][p + 1] * 10) * set_vars[s] for s in all_sets)\n",
+    "            <= int(max_quantities[p][1] * 10000)\n",
+    "        )\n",
     "        model.Add(\n",
-    "            sum(\n",
-    "                int(chemical_set[s][p + 1] * 10) * set_vars[s]\n",
-    "                for s in all_sets) >= int(max_quantities[p][1] * 10000) -\n",
-    "            epsilon)\n",
+    "            sum(int(chemical_set[s][p + 1] * 10) * set_vars[s] for s in all_sets)\n",
+    "            >= int(max_quantities[p][1] * 10000) - epsilon\n",
+    "        )\n",
     "\n",
     "    model.Minimize(epsilon)\n",
     "\n",
@@ -158,15 +162,15 @@
     "    print(f\"Optimal objective value = {solver.ObjectiveValue() / 10000.0}\")\n",
     "\n",
     "    for s in all_sets:\n",
-    "        print(f\"  {chemical_set[s][0]} = {solver.Value(set_vars[s]) / 1000.0}\",\n",
-    "              end=\" \")\n",
+    "        print(f\"  {chemical_set[s][0]} = {solver.Value(set_vars[s]) / 1000.0}\", end=\" \")\n",
     "        print()\n",
     "    for p in all_products:\n",
     "        name = max_quantities[p][0]\n",
     "        max_quantity = max_quantities[p][1]\n",
     "        quantity = sum(\n",
     "            solver.Value(set_vars[s]) / 1000.0 * chemical_set[s][p + 1]\n",
-    "            for s in all_sets)\n",
+    "            for s in all_sets\n",
+    "        )\n",
     "        print(f\"{name}: {quantity} out of {max_quantity}\")\n",
     "\n",
     "\n",
diff --git a/examples/notebook/examples/clustering_sat.ipynb b/examples/notebook/examples/clustering_sat.ipynb
index a3e67892df..f66351f856 100644
--- a/examples/notebook/examples/clustering_sat.ipynb
+++ b/examples/notebook/examples/clustering_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Cluster 40 cities in 4 equal groups to minimize sum of crossed distances.\n"
    ]
   },
@@ -87,6 +88,7 @@
     "\n",
     "\n",
     "distance_matrix = [\n",
+    "    # fmt:off\n",
     "    [0, 10938, 4542, 2835, 29441, 2171, 1611, 9208, 9528, 11111, 16120, 22606, 22127, 20627, 21246, 23387, 16697, 33609, 26184, 24772, 22644, 20655, 30492, 23296, 32979, 18141, 19248, 17129, 17192, 15645, 12658, 11210, 12094, 13175, 18162, 4968, 12308, 10084, 13026, 15056],\n",
     "    [10938, 0, 6422, 9742, 18988, 12974, 11216, 19715, 19004, 18271, 25070, 31971, 31632, 30571, 31578, 33841, 27315, 43964, 36944, 35689, 33569, 31481, 41360, 33760, 43631, 28730, 29976, 27803, 28076, 26408, 23504, 22025, 22000, 13197, 14936, 15146, 23246, 20956, 23963, 25994],\n",
     "    [4542, 6422, 0, 3644, 25173, 6552, 5092, 13584, 13372, 13766, 19805, 26537, 26117, 24804, 25590, 27784, 21148, 37981, 30693, 29315, 27148, 25071, 34943, 27472, 37281, 22389, 23592, 21433, 21655, 20011, 17087, 15612, 15872, 11653, 15666, 8842, 16843, 14618, 17563, 19589],\n",
@@ -127,13 +129,14 @@
     "    [10084, 20956, 14618, 12135, 38935, 8306, 9793, 2615, 5850, 10467, 9918, 14568, 13907, 11803, 11750, 13657, 6901, 23862, 16125, 14748, 12981, 11624, 21033, 15358, 24144, 10304, 10742, 9094, 8042, 7408, 4580, 4072, 8446, 20543, 26181, 7668, 2747, 0, 3330, 5313],\n",
     "    [13026, 23963, 17563, 14771, 42160, 11069, 12925, 5730, 8778, 13375, 11235, 14366, 13621, 11188, 10424, 11907, 5609, 21861, 13624, 11781, 9718, 8304, 17737, 12200, 20816, 7330, 7532, 6117, 4735, 4488, 2599, 3355, 7773, 22186, 27895, 9742, 726, 3330, 0, 2042],\n",
     "    [15056, 25994, 19589, 16743, 44198, 13078, 14967, 7552, 10422, 14935, 11891, 14002, 13225, 10671, 9475, 10633, 5084, 20315, 11866, 9802, 7682, 6471, 15720, 10674, 18908, 6204, 6000, 5066, 3039, 3721, 3496, 4772, 8614, 23805, 29519, 11614, 2749, 5313, 2042, 0],\n",
-    "]  # yapf: disable\n",
+    "    # fmt:on\n",
+    "]\n",
     "\n",
     "\n",
     "def clustering_sat():\n",
     "    \"\"\"Entry point of the program.\"\"\"\n",
     "    num_nodes = len(distance_matrix)\n",
-    "    print('Num nodes =', num_nodes)\n",
+    "    print(\"Num nodes =\", num_nodes)\n",
     "\n",
     "    # Number of groups to split the nodes, must divide num_nodes.\n",
     "    num_groups = 4\n",
@@ -148,7 +151,7 @@
     "    obj_coeffs = []\n",
     "    for n1 in range(num_nodes - 1):\n",
     "        for n2 in range(n1 + 1, num_nodes):\n",
-    "            same = model.NewBoolVar('neighbors_%i_%i' % (n1, n2))\n",
+    "            same = model.NewBoolVar(\"neighbors_%i_%i\" % (n1, n2))\n",
     "            neighbors[n1, n2] = same\n",
     "            obj_vars.append(same)\n",
     "            obj_coeffs.append(distance_matrix[n1][n2] + distance_matrix[n2][n1])\n",
@@ -156,23 +159,24 @@
     "    # Number of neighborss:\n",
     "    for n in range(num_nodes):\n",
     "        model.Add(\n",
-    "            sum(neighbors[m, n] for m in range(n)) +\n",
-    "            sum(neighbors[n, m]\n",
-    "                for m in range(n + 1, num_nodes)) == group_size - 1)\n",
+    "            sum(neighbors[m, n] for m in range(n))\n",
+    "            + sum(neighbors[n, m] for m in range(n + 1, num_nodes))\n",
+    "            == group_size - 1\n",
+    "        )\n",
     "\n",
     "    # Enforce transivity on all triplets.\n",
     "    for n1 in range(num_nodes - 2):\n",
     "        for n2 in range(n1 + 1, num_nodes - 1):\n",
     "            for n3 in range(n2 + 1, num_nodes):\n",
-    "                model.Add(neighbors[n1, n3] + neighbors[n2, n3] +\n",
-    "                          neighbors[n1, n2] != 2)\n",
+    "                model.Add(\n",
+    "                    neighbors[n1, n3] + neighbors[n2, n3] + neighbors[n1, n2] != 2\n",
+    "                )\n",
     "\n",
     "    # Redundant constraints on total sum of neighborss.\n",
     "    model.Add(sum(obj_vars) == num_groups * group_size * (group_size - 1) // 2)\n",
     "\n",
     "    # Minimize weighted sum of arcs.\n",
-    "    model.Minimize(\n",
-    "        sum(obj_vars[i] * obj_coeffs[i] for i in range(len(obj_vars))))\n",
+    "    model.Minimize(sum(obj_vars[i] * obj_coeffs[i] for i in range(len(obj_vars))))\n",
     "\n",
     "    # Solve and print out the solution.\n",
     "    solver = cp_model.CpSolver()\n",
@@ -192,14 +196,14 @@
     "                    for o in range(n + 1, num_nodes):\n",
     "                        if solver.BooleanValue(neighbors[n, o]):\n",
     "                            visited.add(o)\n",
-    "                            output += ' ' + str(o)\n",
-    "                    print('Group', g, ':', output)\n",
+    "                            output += \" \" + str(o)\n",
+    "                    print(\"Group\", g, \":\", output)\n",
     "                    break\n",
     "\n",
     "\n",
     "def main(argv: Sequence[str]) -> None:\n",
     "    if len(argv) > 1:\n",
-    "        raise app.UsageError('Too many command-line arguments.')\n",
+    "        raise app.UsageError(\"Too many command-line arguments.\")\n",
     "    clustering_sat()\n",
     "\n",
     "\n",
diff --git a/examples/notebook/examples/cover_rectangle_sat.ipynb b/examples/notebook/examples/cover_rectangle_sat.ipynb
index a84e0eebde..509e3717ca 100644
--- a/examples/notebook/examples/cover_rectangle_sat.ipynb
+++ b/examples/notebook/examples/cover_rectangle_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Fill a 60x50 rectangle by a minimum number of non-overlapping squares.\n"
    ]
   },
@@ -102,16 +103,16 @@
     "\n",
     "    # Creates intervals for the NoOverlap2D and size variables.\n",
     "    for i in range(num_squares):\n",
-    "        size = model.NewIntVar(1, size_y, 'size_%i' % i)\n",
-    "        start_x = model.NewIntVar(0, size_x, 'sx_%i' % i)\n",
-    "        end_x = model.NewIntVar(0, size_x, 'ex_%i' % i)\n",
-    "        start_y = model.NewIntVar(0, size_y, 'sy_%i' % i)\n",
-    "        end_y = model.NewIntVar(0, size_y, 'ey_%i' % i)\n",
+    "        size = model.NewIntVar(1, size_y, \"size_%i\" % i)\n",
+    "        start_x = model.NewIntVar(0, size_x, \"sx_%i\" % i)\n",
+    "        end_x = model.NewIntVar(0, size_x, \"ex_%i\" % i)\n",
+    "        start_y = model.NewIntVar(0, size_y, \"sy_%i\" % i)\n",
+    "        end_y = model.NewIntVar(0, size_y, \"ey_%i\" % i)\n",
     "\n",
-    "        interval_x = model.NewIntervalVar(start_x, size, end_x, 'ix_%i' % i)\n",
-    "        interval_y = model.NewIntervalVar(start_y, size, end_y, 'iy_%i' % i)\n",
+    "        interval_x = model.NewIntervalVar(start_x, size, end_x, \"ix_%i\" % i)\n",
+    "        interval_y = model.NewIntervalVar(start_y, size, end_y, \"iy_%i\" % i)\n",
     "\n",
-    "        area = model.NewIntVar(1, size_y * size_y, 'area_%i' % i)\n",
+    "        area = model.NewIntVar(1, size_y * size_y, \"area_%i\" % i)\n",
     "        model.AddMultiplicationEquality(area, [size, size])\n",
     "\n",
     "        areas.append(area)\n",
@@ -136,7 +137,7 @@
     "        model.Add(sizes[i] <= sizes[i + 1])\n",
     "\n",
     "        # Define same to be true iff sizes[i] == sizes[i + 1]\n",
-    "        same = model.NewBoolVar('')\n",
+    "        same = model.NewBoolVar(\"\")\n",
     "        model.Add(sizes[i] == sizes[i + 1]).OnlyEnforceIf(same)\n",
     "        model.Add(sizes[i] < sizes[i + 1]).OnlyEnforceIf(same.Not())\n",
     "\n",
@@ -149,35 +150,39 @@
     "\n",
     "    # Creates a solver and solves.\n",
     "    solver = cp_model.CpSolver()\n",
-    "    solver.parameters.num_workers = 8\n",
+    "    solver.parameters.num_workers = 16\n",
+    "    # solver.parameters.log_search_progress = True\n",
+    "    solver.parameters.max_time_in_seconds = 10.0\n",
     "    status = solver.Solve(model)\n",
-    "    print('%s found in %0.2fs' % (solver.StatusName(status), solver.WallTime()))\n",
+    "    print(\"%s found in %0.2fs\" % (solver.StatusName(status), solver.WallTime()))\n",
     "\n",
     "    # Prints solution.\n",
-    "    if status == cp_model.OPTIMAL:\n",
-    "        display = [[' ' for _ in range(size_x)] for _ in range(size_y)]\n",
+    "    if status == cp_model.OPTIMAL or status == cp_model.FEASIBLE:\n",
+    "        display = [[\" \" for _ in range(size_x)] for _ in range(size_y)]\n",
     "        for i in range(num_squares):\n",
     "            sol_x = solver.Value(x_starts[i])\n",
     "            sol_y = solver.Value(y_starts[i])\n",
     "            sol_s = solver.Value(sizes[i])\n",
-    "            char = format(i, '01x')\n",
+    "            char = format(i, \"01x\")\n",
     "            for j in range(sol_s):\n",
     "                for k in range(sol_s):\n",
-    "                    if display[sol_y + j][sol_x + k] != ' ':\n",
-    "                        print('ERROR between %s and %s' %\n",
-    "                              (display[sol_y + j][sol_x + k], char))\n",
+    "                    if display[sol_y + j][sol_x + k] != \" \":\n",
+    "                        print(\n",
+    "                            \"ERROR between %s and %s\"\n",
+    "                            % (display[sol_y + j][sol_x + k], char)\n",
+    "                        )\n",
     "                    display[sol_y + j][sol_x + k] = char\n",
     "\n",
     "        for line in range(size_y):\n",
-    "            print(' '.join(display[line]))\n",
+    "            print(\" \".join(display[line]))\n",
     "    return status == cp_model.OPTIMAL\n",
     "\n",
     "\n",
     "def main(argv: Sequence[str]) -> None:\n",
     "    if len(argv) > 1:\n",
-    "        raise app.UsageError('Too many command-line arguments.')\n",
+    "        raise app.UsageError(\"Too many command-line arguments.\")\n",
     "    for num_squares in range(1, 15):\n",
-    "        print('Trying with size =', num_squares)\n",
+    "        print(\"Trying with size =\", num_squares)\n",
     "        if cover_rectangle(num_squares):\n",
     "            break\n",
     "\n",
diff --git a/examples/notebook/examples/cryptarithm_sat.ipynb b/examples/notebook/examples/cryptarithm_sat.ipynb
index 7b5cf93a59..ac79fe69ba 100644
--- a/examples/notebook/examples/cryptarithm_sat.ipynb
+++ b/examples/notebook/examples/cryptarithm_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Use CP-SAT to solve a simple cryptarithmetic problem: SEND+MORE=MONEY.\n",
     "\n"
    ]
@@ -87,28 +88,27 @@
     "\n",
     "\n",
     "def send_more_money():\n",
-    "    \"\"\"Solve the cryptarithmic puzzle SEND+MORE=MONEY.\n",
-    "  \"\"\"\n",
+    "    \"\"\"Solve the cryptarithmic puzzle SEND+MORE=MONEY.\"\"\"\n",
     "    model = cp_model.CpModel()\n",
     "\n",
     "    # Create variables.\n",
     "    # Since s is a leading digit, it can't be 0.\n",
-    "    s = model.NewIntVar(1, 9, 's')\n",
-    "    e = model.NewIntVar(0, 9, 'e')\n",
-    "    n = model.NewIntVar(0, 9, 'n')\n",
-    "    d = model.NewIntVar(0, 9, 'd')\n",
+    "    s = model.NewIntVar(1, 9, \"s\")\n",
+    "    e = model.NewIntVar(0, 9, \"e\")\n",
+    "    n = model.NewIntVar(0, 9, \"n\")\n",
+    "    d = model.NewIntVar(0, 9, \"d\")\n",
     "    # Since m is a leading digit, it can't be 0.\n",
-    "    m = model.NewIntVar(1, 9, 'm')\n",
-    "    o = model.NewIntVar(0, 9, 'o')\n",
-    "    r = model.NewIntVar(0, 9, 'r')\n",
-    "    y = model.NewIntVar(0, 9, 'y')\n",
+    "    m = model.NewIntVar(1, 9, \"m\")\n",
+    "    o = model.NewIntVar(0, 9, \"o\")\n",
+    "    r = model.NewIntVar(0, 9, \"r\")\n",
+    "    y = model.NewIntVar(0, 9, \"y\")\n",
     "\n",
     "    # Create carry variables. c0 is true if the first column of addends carries\n",
     "    # a 1, c2 is true if the second column carries a 1, and so on.\n",
-    "    c0 = model.NewBoolVar('c0')\n",
-    "    c1 = model.NewBoolVar('c1')\n",
-    "    c2 = model.NewBoolVar('c2')\n",
-    "    c3 = model.NewBoolVar('c3')\n",
+    "    c0 = model.NewBoolVar(\"c0\")\n",
+    "    c1 = model.NewBoolVar(\"c1\")\n",
+    "    c2 = model.NewBoolVar(\"c2\")\n",
+    "    c3 = model.NewBoolVar(\"c3\")\n",
     "\n",
     "    # Force all letters to take on different values.\n",
     "    model.AddAllDifferent(s, e, n, d, m, o, r, y)\n",
@@ -131,15 +131,15 @@
     "    # Solve model.\n",
     "    solver = cp_model.CpSolver()\n",
     "    if solver.Solve(model) == cp_model.OPTIMAL:\n",
-    "        print('Optimal solution found!')\n",
-    "    print('s:', solver.Value(s))\n",
-    "    print('e:', solver.Value(e))\n",
-    "    print('n:', solver.Value(n))\n",
-    "    print('d:', solver.Value(d))\n",
-    "    print('m:', solver.Value(m))\n",
-    "    print('o:', solver.Value(o))\n",
-    "    print('r:', solver.Value(r))\n",
-    "    print('y:', solver.Value(y))\n",
+    "        print(\"Optimal solution found!\")\n",
+    "    print(\"s:\", solver.Value(s))\n",
+    "    print(\"e:\", solver.Value(e))\n",
+    "    print(\"n:\", solver.Value(n))\n",
+    "    print(\"d:\", solver.Value(d))\n",
+    "    print(\"m:\", solver.Value(m))\n",
+    "    print(\"o:\", solver.Value(o))\n",
+    "    print(\"r:\", solver.Value(r))\n",
+    "    print(\"y:\", solver.Value(y))\n",
     "\n",
     "\n",
     "def main(_):\n",
diff --git a/examples/notebook/examples/flexible_job_shop_sat.ipynb b/examples/notebook/examples/flexible_job_shop_sat.ipynb
index 5d7d02d10c..db764f209a 100644
--- a/examples/notebook/examples/flexible_job_shop_sat.ipynb
+++ b/examples/notebook/examples/flexible_job_shop_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Solves a flexible jobshop problems with the CP-SAT solver.\n",
     "\n",
     "A jobshop is a standard scheduling problem when you must sequence a\n",
@@ -106,8 +107,10 @@
     "\n",
     "    def on_solution_callback(self):\n",
     "        \"\"\"Called at each new solution.\"\"\"\n",
-    "        print('Solution %i, time = %f s, objective = %i' %\n",
-    "              (self.__solution_count, self.WallTime(), self.ObjectiveValue()))\n",
+    "        print(\n",
+    "            \"Solution %i, time = %f s, objective = %i\"\n",
+    "            % (self.__solution_count, self.WallTime(), self.ObjectiveValue())\n",
+    "        )\n",
     "        self.__solution_count += 1\n",
     "\n",
     "\n",
@@ -149,7 +152,7 @@
     "                max_task_duration = max(max_task_duration, alternative[0])\n",
     "            horizon += max_task_duration\n",
     "\n",
-    "    print('Horizon = %i' % horizon)\n",
+    "    print(\"Horizon = %i\" % horizon)\n",
     "\n",
     "    # Global storage of variables.\n",
     "    intervals_per_resources = collections.defaultdict(list)\n",
@@ -177,13 +180,15 @@
     "                max_duration = max(max_duration, alt_duration)\n",
     "\n",
     "            # Create main interval for the task.\n",
-    "            suffix_name = '_j%i_t%i' % (job_id, task_id)\n",
-    "            start = model.NewIntVar(0, horizon, 'start' + suffix_name)\n",
-    "            duration = model.NewIntVar(min_duration, max_duration,\n",
-    "                                       'duration' + suffix_name)\n",
-    "            end = model.NewIntVar(0, horizon, 'end' + suffix_name)\n",
-    "            interval = model.NewIntervalVar(start, duration, end,\n",
-    "                                            'interval' + suffix_name)\n",
+    "            suffix_name = \"_j%i_t%i\" % (job_id, task_id)\n",
+    "            start = model.NewIntVar(0, horizon, \"start\" + suffix_name)\n",
+    "            duration = model.NewIntVar(\n",
+    "                min_duration, max_duration, \"duration\" + suffix_name\n",
+    "            )\n",
+    "            end = model.NewIntVar(0, horizon, \"end\" + suffix_name)\n",
+    "            interval = model.NewIntervalVar(\n",
+    "                start, duration, end, \"interval\" + suffix_name\n",
+    "            )\n",
     "\n",
     "            # Store the start for the solution.\n",
     "            starts[(job_id, task_id)] = start\n",
@@ -197,14 +202,14 @@
     "            if num_alternatives > 1:\n",
     "                l_presences = []\n",
     "                for alt_id in all_alternatives:\n",
-    "                    alt_suffix = '_j%i_t%i_a%i' % (job_id, task_id, alt_id)\n",
-    "                    l_presence = model.NewBoolVar('presence' + alt_suffix)\n",
-    "                    l_start = model.NewIntVar(0, horizon, 'start' + alt_suffix)\n",
+    "                    alt_suffix = \"_j%i_t%i_a%i\" % (job_id, task_id, alt_id)\n",
+    "                    l_presence = model.NewBoolVar(\"presence\" + alt_suffix)\n",
+    "                    l_start = model.NewIntVar(0, horizon, \"start\" + alt_suffix)\n",
     "                    l_duration = task[alt_id][0]\n",
-    "                    l_end = model.NewIntVar(0, horizon, 'end' + alt_suffix)\n",
+    "                    l_end = model.NewIntVar(0, horizon, \"end\" + alt_suffix)\n",
     "                    l_interval = model.NewOptionalIntervalVar(\n",
-    "                        l_start, l_duration, l_end, l_presence,\n",
-    "                        'interval' + alt_suffix)\n",
+    "                        l_start, l_duration, l_end, l_presence, \"interval\" + alt_suffix\n",
+    "                    )\n",
     "                    l_presences.append(l_presence)\n",
     "\n",
     "                    # Link the primary/global variables with the local ones.\n",
@@ -233,7 +238,7 @@
     "            model.AddNoOverlap(intervals)\n",
     "\n",
     "    # Makespan objective\n",
-    "    makespan = model.NewIntVar(0, horizon, 'makespan')\n",
+    "    makespan = model.NewIntVar(0, horizon, \"makespan\")\n",
     "    model.AddMaxEquality(makespan, job_ends)\n",
     "    model.Minimize(makespan)\n",
     "\n",
@@ -244,7 +249,7 @@
     "\n",
     "    # Print final solution.\n",
     "    for job_id in all_jobs:\n",
-    "        print('Job %i:' % job_id)\n",
+    "        print(\"Job %i:\" % job_id)\n",
     "        for task_id in range(len(jobs[job_id])):\n",
     "            start_value = solver.Value(starts[(job_id, task_id)])\n",
     "            machine = -1\n",
@@ -256,15 +261,16 @@
     "                    machine = jobs[job_id][task_id][alt_id][1]\n",
     "                    selected = alt_id\n",
     "            print(\n",
-    "                '  task_%i_%i starts at %i (alt %i, machine %i, duration %i)' %\n",
-    "                (job_id, task_id, start_value, selected, machine, duration))\n",
+    "                \"  task_%i_%i starts at %i (alt %i, machine %i, duration %i)\"\n",
+    "                % (job_id, task_id, start_value, selected, machine, duration)\n",
+    "            )\n",
     "\n",
-    "    print('Solve status: %s' % solver.StatusName(status))\n",
-    "    print('Optimal objective value: %i' % solver.ObjectiveValue())\n",
-    "    print('Statistics')\n",
-    "    print('  - conflicts : %i' % solver.NumConflicts())\n",
-    "    print('  - branches  : %i' % solver.NumBranches())\n",
-    "    print('  - wall time : %f s' % solver.WallTime())\n",
+    "    print(\"Solve status: %s\" % solver.StatusName(status))\n",
+    "    print(\"Optimal objective value: %i\" % solver.ObjectiveValue())\n",
+    "    print(\"Statistics\")\n",
+    "    print(\"  - conflicts : %i\" % solver.NumConflicts())\n",
+    "    print(\"  - branches  : %i\" % solver.NumBranches())\n",
+    "    print(\"  - wall time : %f s\" % solver.WallTime())\n",
     "\n",
     "\n",
     "flexible_jobshop()\n",
diff --git a/examples/notebook/examples/gate_scheduling_sat.ipynb b/examples/notebook/examples/gate_scheduling_sat.ipynb
index 23fee0520d..f5ebbe7d3a 100644
--- a/examples/notebook/examples/gate_scheduling_sat.ipynb
+++ b/examples/notebook/examples/gate_scheduling_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Gate Scheduling problem.\n",
     "\n",
     "We have a set of jobs to perform (duration, width).\n",
@@ -114,7 +115,7 @@
     "        [1, 2],\n",
     "        [6, 8],\n",
     "        [4, 5],\n",
-    "        [3, 7]\n",
+    "        [3, 7],\n",
     "    ]\n",
     "\n",
     "    max_width = 10\n",
@@ -133,31 +134,31 @@
     "\n",
     "    for i in all_jobs:\n",
     "        # Create main interval.\n",
-    "        start = model.NewIntVar(0, horizon, 'start_%i' % i)\n",
+    "        start = model.NewIntVar(0, horizon, \"start_%i\" % i)\n",
     "        duration = jobs[i][0]\n",
-    "        end = model.NewIntVar(0, horizon, 'end_%i' % i)\n",
-    "        interval = model.NewIntervalVar(start, duration, end, 'interval_%i' % i)\n",
+    "        end = model.NewIntVar(0, horizon, \"end_%i\" % i)\n",
+    "        interval = model.NewIntervalVar(start, duration, end, \"interval_%i\" % i)\n",
     "        starts.append(start)\n",
     "        intervals.append(interval)\n",
     "        ends.append(end)\n",
     "        demands.append(jobs[i][1])\n",
     "\n",
     "        # Create an optional copy of interval to be executed on machine 0.\n",
-    "        performed_on_m0 = model.NewBoolVar('perform_%i_on_m0' % i)\n",
+    "        performed_on_m0 = model.NewBoolVar(\"perform_%i_on_m0\" % i)\n",
     "        performed.append(performed_on_m0)\n",
-    "        start0 = model.NewIntVar(0, horizon, 'start_%i_on_m0' % i)\n",
-    "        end0 = model.NewIntVar(0, horizon, 'end_%i_on_m0' % i)\n",
-    "        interval0 = model.NewOptionalIntervalVar(start0, duration, end0,\n",
-    "                                                 performed_on_m0,\n",
-    "                                                 'interval_%i_on_m0' % i)\n",
+    "        start0 = model.NewIntVar(0, horizon, \"start_%i_on_m0\" % i)\n",
+    "        end0 = model.NewIntVar(0, horizon, \"end_%i_on_m0\" % i)\n",
+    "        interval0 = model.NewOptionalIntervalVar(\n",
+    "            start0, duration, end0, performed_on_m0, \"interval_%i_on_m0\" % i\n",
+    "        )\n",
     "        intervals0.append(interval0)\n",
     "\n",
     "        # Create an optional copy of interval to be executed on machine 1.\n",
-    "        start1 = model.NewIntVar(0, horizon, 'start_%i_on_m1' % i)\n",
-    "        end1 = model.NewIntVar(0, horizon, 'end_%i_on_m1' % i)\n",
-    "        interval1 = model.NewOptionalIntervalVar(start1, duration, end1,\n",
-    "                                                 performed_on_m0.Not(),\n",
-    "                                                 'interval_%i_on_m1' % i)\n",
+    "        start1 = model.NewIntVar(0, horizon, \"start_%i_on_m1\" % i)\n",
+    "        end1 = model.NewIntVar(0, horizon, \"end_%i_on_m1\" % i)\n",
+    "        interval1 = model.NewOptionalIntervalVar(\n",
+    "            start1, duration, end1, performed_on_m0.Not(), \"interval_%i_on_m1\" % i\n",
+    "        )\n",
     "        intervals1.append(interval1)\n",
     "\n",
     "        # We only propagate the constraint if the tasks is performed on the machine.\n",
@@ -172,7 +173,7 @@
     "    model.AddNoOverlap(intervals1)\n",
     "\n",
     "    # Objective variable.\n",
-    "    makespan = model.NewIntVar(0, horizon, 'makespan')\n",
+    "    makespan = model.NewIntVar(0, horizon, \"makespan\")\n",
     "    model.AddMaxEquality(makespan, ends)\n",
     "    model.Minimize(makespan)\n",
     "\n",
@@ -185,9 +186,8 @@
     "\n",
     "    # Output solution.\n",
     "    if visualization.RunFromIPython():\n",
-    "        output = visualization.SvgWrapper(solver.ObjectiveValue(), max_width,\n",
-    "                                          40.0)\n",
-    "        output.AddTitle('Makespan = %i' % solver.ObjectiveValue())\n",
+    "        output = visualization.SvgWrapper(solver.ObjectiveValue(), max_width, 40.0)\n",
+    "        output.AddTitle(\"Makespan = %i\" % solver.ObjectiveValue())\n",
     "        color_manager = visualization.ColorManager()\n",
     "        color_manager.SeedRandomColor(0)\n",
     "\n",
@@ -197,24 +197,26 @@
     "            d_x = jobs[i][0]\n",
     "            d_y = jobs[i][1]\n",
     "            s_y = performed_machine * (max_width - d_y)\n",
-    "            output.AddRectangle(start, s_y, d_x, d_y,\n",
-    "                                color_manager.RandomColor(), 'black', 'j%i' % i)\n",
+    "            output.AddRectangle(\n",
+    "                start, s_y, d_x, d_y, color_manager.RandomColor(), \"black\", \"j%i\" % i\n",
+    "            )\n",
     "\n",
     "        output.AddXScale()\n",
     "        output.AddYScale()\n",
     "        output.Display()\n",
     "    else:\n",
-    "        print('Solution')\n",
-    "        print('  - makespan = %i' % solver.ObjectiveValue())\n",
+    "        print(\"Solution\")\n",
+    "        print(\"  - makespan = %i\" % solver.ObjectiveValue())\n",
     "        for i in all_jobs:\n",
     "            performed_machine = 1 - solver.Value(performed[i])\n",
     "            start = solver.Value(starts[i])\n",
-    "            print('  - Job %i starts at %i on machine %i' %\n",
-    "                  (i, start, performed_machine))\n",
-    "        print('Statistics')\n",
-    "        print('  - conflicts : %i' % solver.NumConflicts())\n",
-    "        print('  - branches  : %i' % solver.NumBranches())\n",
-    "        print('  - wall time : %f s' % solver.WallTime())\n",
+    "            print(\n",
+    "                \"  - Job %i starts at %i on machine %i\" % (i, start, performed_machine)\n",
+    "            )\n",
+    "        print(\"Statistics\")\n",
+    "        print(\"  - conflicts : %i\" % solver.NumConflicts())\n",
+    "        print(\"  - branches  : %i\" % solver.NumBranches())\n",
+    "        print(\"  - wall time : %f s\" % solver.WallTime())\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/examples/golomb8.ipynb b/examples/notebook/examples/golomb8.ipynb
index cd41ed6301..07aced541e 100644
--- a/examples/notebook/examples/golomb8.ipynb
+++ b/examples/notebook/examples/golomb8.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "This is the Golomb ruler problem.\n",
     "\n",
     "This model aims at maximizing radar interferences in a minimum space.\n",
@@ -96,13 +97,13 @@
     "\n",
     "def main(_):\n",
     "    # Create the solver.\n",
-    "    solver = pywrapcp.Solver('golomb ruler')\n",
+    "    solver = pywrapcp.Solver(\"golomb ruler\")\n",
     "\n",
     "    size = 8\n",
     "    var_max = size * size\n",
     "    all_vars = list(range(0, size))\n",
     "\n",
-    "    marks = [solver.IntVar(0, var_max, 'marks_%d' % i) for i in all_vars]\n",
+    "    marks = [solver.IntVar(0, var_max, \"marks_%d\" % i) for i in all_vars]\n",
     "\n",
     "    objective = solver.Minimize(marks[size - 1], 1)\n",
     "\n",
@@ -124,20 +125,27 @@
     "    collector = solver.AllSolutionCollector(solution)\n",
     "\n",
     "    solver.Solve(\n",
-    "        solver.Phase(marks, solver.CHOOSE_FIRST_UNBOUND,\n",
-    "                     solver.ASSIGN_MIN_VALUE), [objective, collector])\n",
+    "        solver.Phase(marks, solver.CHOOSE_FIRST_UNBOUND, solver.ASSIGN_MIN_VALUE),\n",
+    "        [objective, collector],\n",
+    "    )\n",
     "    for i in range(0, collector.SolutionCount()):\n",
     "        obj_value = collector.Value(i, marks[size - 1])\n",
     "        time = collector.WallTime(i)\n",
     "        branches = collector.Branches(i)\n",
     "        failures = collector.Failures(i)\n",
-    "        print(('Solution #%i: value = %i, failures = %i, branches = %i,'\n",
-    "               'time = %i ms') % (i, obj_value, failures, branches, time))\n",
+    "        print(\n",
+    "            (\"Solution #%i: value = %i, failures = %i, branches = %i,\" \"time = %i ms\")\n",
+    "            % (i, obj_value, failures, branches, time)\n",
+    "        )\n",
     "    time = solver.WallTime()\n",
     "    branches = solver.Branches()\n",
     "    failures = solver.Failures()\n",
-    "    print(('Total run : failures = %i, branches = %i, time = %i ms' %\n",
-    "           (failures, branches, time)))\n",
+    "    print(\n",
+    "        (\n",
+    "            \"Total run : failures = %i, branches = %i, time = %i ms\"\n",
+    "            % (failures, branches, time)\n",
+    "        )\n",
+    "    )\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/examples/golomb_sat.ipynb b/examples/notebook/examples/golomb_sat.ipynb
index 0902a6667d..b36fb3ab02 100644
--- a/examples/notebook/examples/golomb_sat.ipynb
+++ b/examples/notebook/examples/golomb_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "This is the Golomb ruler problem.\n",
     "\n",
     "This model aims at maximizing radar interferences in a minimum space.\n",
@@ -95,11 +96,12 @@
     "from google.protobuf import text_format\n",
     "from ortools.sat.python import cp_model\n",
     "\n",
-    "_ORDER = flags.DEFINE_integer('order', 8, 'Order of the ruler.')\n",
+    "_ORDER = flags.DEFINE_integer(\"order\", 8, \"Order of the ruler.\")\n",
     "_PARAMS = flags.DEFINE_string(\n",
-    "    'params',\n",
-    "    'num_search_workers:16,log_search_progress:true,max_time_in_seconds:45',\n",
-    "    'Sat solver parameters.')\n",
+    "    \"params\",\n",
+    "    \"num_search_workers:16,log_search_progress:true,max_time_in_seconds:45\",\n",
+    "    \"Sat solver parameters.\",\n",
+    ")\n",
     "\n",
     "\n",
     "def solve_golomb_ruler(order, params):\n",
@@ -110,7 +112,7 @@
     "    var_max = order * order\n",
     "    all_vars = list(range(0, order))\n",
     "\n",
-    "    marks = [model.NewIntVar(0, var_max, f'marks_{i}') for i in all_vars]\n",
+    "    marks = [model.NewIntVar(0, var_max, f\"marks_{i}\") for i in all_vars]\n",
     "\n",
     "    model.Add(marks[0] == 0)\n",
     "    for i in range(order - 2):\n",
@@ -119,7 +121,7 @@
     "    diffs = []\n",
     "    for i in range(order - 1):\n",
     "        for j in range(i + 1, order):\n",
-    "            diff = model.NewIntVar(0, var_max, f'diff [{j},{i}]')\n",
+    "            diff = model.NewIntVar(0, var_max, f\"diff [{j},{i}]\")\n",
     "            model.Add(diff == marks[j] - marks[i])\n",
     "            diffs.append(diff)\n",
     "    model.AddAllDifferent(diffs)\n",
@@ -136,27 +138,27 @@
     "    if params:\n",
     "        text_format.Parse(params, solver.parameters)\n",
     "    solution_printer = cp_model.ObjectiveSolutionPrinter()\n",
-    "    print(f'Golomb ruler(order={order})')\n",
+    "    print(f\"Golomb ruler(order={order})\")\n",
     "    status = solver.Solve(model, solution_printer)\n",
     "\n",
     "    # Print solution.\n",
-    "    print(f'status: {solver.StatusName(status)}')\n",
+    "    print(f\"status: {solver.StatusName(status)}\")\n",
     "    if status in (cp_model.OPTIMAL, cp_model.FEASIBLE):\n",
     "        for idx, var in enumerate(marks):\n",
-    "            print(f'mark[{idx}]: {solver.Value(var)}')\n",
+    "            print(f\"mark[{idx}]: {solver.Value(var)}\")\n",
     "        intervals = [solver.Value(diff) for diff in diffs]\n",
     "        intervals.sort()\n",
-    "        print(f'intervals: {intervals}')\n",
+    "        print(f\"intervals: {intervals}\")\n",
     "\n",
-    "    print('Statistics:')\n",
-    "    print(f'- conflicts: {solver.NumConflicts()}')\n",
-    "    print(f'- branches : {solver.NumBranches()}')\n",
-    "    print(f'- wall time: {solver.WallTime()}s\\n')\n",
+    "    print(\"Statistics:\")\n",
+    "    print(f\"- conflicts: {solver.NumConflicts()}\")\n",
+    "    print(f\"- branches : {solver.NumBranches()}\")\n",
+    "    print(f\"- wall time: {solver.WallTime()}s\\n\")\n",
     "\n",
     "\n",
     "def main(argv: Sequence[str]) -> None:\n",
     "    if len(argv) > 1:\n",
-    "        raise app.UsageError('Too many command-line arguments.')\n",
+    "        raise app.UsageError(\"Too many command-line arguments.\")\n",
     "    solve_golomb_ruler(_ORDER.value, _PARAMS.value)\n",
     "\n",
     "\n",
diff --git a/examples/notebook/examples/hidato_sat.ipynb b/examples/notebook/examples/hidato_sat.ipynb
index 5738d1ca06..96b83c0287 100644
--- a/examples/notebook/examples/hidato_sat.ipynb
+++ b/examples/notebook/examples/hidato_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Solves the Hidato problem with the CP-SAT solver.\n"
    ]
   },
@@ -89,25 +90,29 @@
     "def build_pairs(rows, cols):\n",
     "    \"\"\"Build closeness pairs for consecutive numbers.\n",
     "\n",
-    "  Build set of allowed pairs such that two consecutive numbers touch\n",
-    "  each other in the grid.\n",
+    "    Build set of allowed pairs such that two consecutive numbers touch\n",
+    "    each other in the grid.\n",
     "\n",
-    "  Returns:\n",
-    "    A list of pairs for allowed consecutive position of numbers.\n",
+    "    Returns:\n",
+    "      A list of pairs for allowed consecutive position of numbers.\n",
     "\n",
-    "  Args:\n",
-    "    rows: the number of rows in the grid\n",
-    "    cols: the number of columns in the grid\n",
-    "  \"\"\"\n",
+    "    Args:\n",
+    "      rows: the number of rows in the grid\n",
+    "      cols: the number of columns in the grid\n",
+    "    \"\"\"\n",
     "    result = []\n",
     "    for x in range(rows):\n",
     "        for y in range(cols):\n",
     "            for dx in (-1, 0, 1):\n",
     "                for dy in (-1, 0, 1):\n",
-    "                    if (x + dx >= 0 and x + dx < rows and y + dy >= 0 and\n",
-    "                            y + dy < cols and (dx != 0 or dy != 0)):\n",
-    "                        result.append(\n",
-    "                            (x * cols + y, (x + dx) * cols + (y + dy)))\n",
+    "                    if (\n",
+    "                        x + dx >= 0\n",
+    "                        and x + dx < rows\n",
+    "                        and y + dy >= 0\n",
+    "                        and y + dy < cols\n",
+    "                        and (dx != 0 or dy != 0)\n",
+    "                    ):\n",
+    "                        result.append((x * cols + y, (x + dx) * cols + (y + dy)))\n",
     "    return result\n",
     "\n",
     "\n",
@@ -122,7 +127,7 @@
     "        position = positions[k]\n",
     "        board[position // cols][position % cols] = k + 1\n",
     "    # Print the board.\n",
-    "    print('Solution')\n",
+    "    print(\"Solution\")\n",
     "    print_matrix(board)\n",
     "\n",
     "\n",
@@ -131,12 +136,12 @@
     "    rows = len(game)\n",
     "    cols = len(game[0])\n",
     "    for i in range(rows):\n",
-    "        line = ''\n",
+    "        line = \"\"\n",
     "        for j in range(cols):\n",
     "            if game[i][j] == 0:\n",
-    "                line += '  .'\n",
+    "                line += \"  .\"\n",
     "            else:\n",
-    "                line += '% 3s' % game[i][j]\n",
+    "                line += \"% 3s\" % game[i][j]\n",
     "        print(line)\n",
     "\n",
     "\n",
@@ -152,34 +157,60 @@
     "        puzzle = [[6, 0, 9], [0, 2, 8], [1, 0, 0]]\n",
     "\n",
     "    elif problem == 2:\n",
-    "        puzzle = [[0, 44, 41, 0, 0, 0, 0], [0, 43, 0, 28, 29, 0, 0],\n",
-    "                  [0, 1, 0, 0, 0, 33, 0], [0, 2, 25, 4, 34, 0, 36],\n",
-    "                  [49, 16, 0, 23, 0, 0, 0], [0, 19, 0, 0, 12, 7, 0],\n",
-    "                  [0, 0, 0, 14, 0, 0, 0]]\n",
+    "        puzzle = [\n",
+    "            [0, 44, 41, 0, 0, 0, 0],\n",
+    "            [0, 43, 0, 28, 29, 0, 0],\n",
+    "            [0, 1, 0, 0, 0, 33, 0],\n",
+    "            [0, 2, 25, 4, 34, 0, 36],\n",
+    "            [49, 16, 0, 23, 0, 0, 0],\n",
+    "            [0, 19, 0, 0, 12, 7, 0],\n",
+    "            [0, 0, 0, 14, 0, 0, 0],\n",
+    "        ]\n",
     "\n",
     "    elif problem == 3:\n",
     "        # Problems from the book:\n",
     "        # Gyora Bededek: \"Hidato: 2000 Pure Logic Puzzles\"\n",
     "        # Problem 1 (Practice)\n",
-    "        puzzle = [[0, 0, 20, 0, 0], [0, 0, 0, 16, 18], [22, 0, 15, 0, 0],\n",
-    "                  [23, 0, 1, 14, 11], [0, 25, 0, 0, 12]]\n",
+    "        puzzle = [\n",
+    "            [0, 0, 20, 0, 0],\n",
+    "            [0, 0, 0, 16, 18],\n",
+    "            [22, 0, 15, 0, 0],\n",
+    "            [23, 0, 1, 14, 11],\n",
+    "            [0, 25, 0, 0, 12],\n",
+    "        ]\n",
     "\n",
     "    elif problem == 4:\n",
     "        # problem 2 (Practice)\n",
-    "        puzzle = [[0, 0, 0, 0, 14], [0, 18, 12, 0, 0], [0, 0, 17, 4, 5],\n",
-    "                  [0, 0, 7, 0, 0], [9, 8, 25, 1, 0]]\n",
+    "        puzzle = [\n",
+    "            [0, 0, 0, 0, 14],\n",
+    "            [0, 18, 12, 0, 0],\n",
+    "            [0, 0, 17, 4, 5],\n",
+    "            [0, 0, 7, 0, 0],\n",
+    "            [9, 8, 25, 1, 0],\n",
+    "        ]\n",
     "\n",
     "    elif problem == 5:\n",
     "        # problem 3 (Beginner)\n",
-    "        puzzle = [[0, 26, 0, 0, 0, 18], [0, 0, 27, 0, 0, 19],\n",
-    "                  [31, 23, 0, 0, 14, 0], [0, 33, 8, 0, 15, 1],\n",
-    "                  [0, 0, 0, 5, 0, 0], [35, 36, 0, 10, 0, 0]]\n",
+    "        puzzle = [\n",
+    "            [0, 26, 0, 0, 0, 18],\n",
+    "            [0, 0, 27, 0, 0, 19],\n",
+    "            [31, 23, 0, 0, 14, 0],\n",
+    "            [0, 33, 8, 0, 15, 1],\n",
+    "            [0, 0, 0, 5, 0, 0],\n",
+    "            [35, 36, 0, 10, 0, 0],\n",
+    "        ]\n",
     "    elif problem == 6:\n",
     "        # Problem 15 (Intermediate)\n",
-    "        puzzle = [[64, 0, 0, 0, 0, 0, 0, 0], [1, 63, 0, 59, 15, 57, 53, 0],\n",
-    "                  [0, 4, 0, 14, 0, 0, 0, 0], [3, 0, 11, 0, 20, 19, 0, 50],\n",
-    "                  [0, 0, 0, 0, 22, 0, 48, 40], [9, 0, 0, 32, 23, 0, 0, 41],\n",
-    "                  [27, 0, 0, 0, 36, 0, 46, 0], [28, 30, 0, 35, 0, 0, 0, 0]]\n",
+    "        puzzle = [\n",
+    "            [64, 0, 0, 0, 0, 0, 0, 0],\n",
+    "            [1, 63, 0, 59, 15, 57, 53, 0],\n",
+    "            [0, 4, 0, 14, 0, 0, 0, 0],\n",
+    "            [3, 0, 11, 0, 20, 19, 0, 50],\n",
+    "            [0, 0, 0, 0, 22, 0, 48, 40],\n",
+    "            [9, 0, 0, 32, 23, 0, 0, 41],\n",
+    "            [27, 0, 0, 0, 36, 0, 46, 0],\n",
+    "            [28, 30, 0, 35, 0, 0, 0, 0],\n",
+    "        ]\n",
     "    return puzzle\n",
     "\n",
     "\n",
@@ -191,18 +222,16 @@
     "    r = len(puzzle)\n",
     "    c = len(puzzle[0])\n",
     "    if not visualization.RunFromIPython():\n",
-    "        print('')\n",
-    "        print('----- Solving problem %i -----' % index)\n",
-    "        print('')\n",
-    "        print(('Initial game (%i x %i)' % (r, c)))\n",
+    "        print(\"\")\n",
+    "        print(\"----- Solving problem %i -----\" % index)\n",
+    "        print(\"\")\n",
+    "        print((\"Initial game (%i x %i)\" % (r, c)))\n",
     "        print_matrix(puzzle)\n",
     "\n",
     "    #\n",
     "    # declare variables\n",
     "    #\n",
-    "    positions = [\n",
-    "        model.NewIntVar(0, r * c - 1, 'p[%i]' % i) for i in range(r * c)\n",
-    "    ]\n",
+    "    positions = [model.NewIntVar(0, r * c - 1, \"p[%i]\" % i) for i in range(r * c)]\n",
     "\n",
     "    #\n",
     "    # constraints\n",
@@ -221,8 +250,7 @@
     "    # We use an allowed assignment constraint to model it.\n",
     "    close_tuples = build_pairs(r, c)\n",
     "    for k in range(0, r * c - 1):\n",
-    "        model.AddAllowedAssignments([positions[k], positions[k + 1]],\n",
-    "                                    close_tuples)\n",
+    "        model.AddAllowedAssignments([positions[k], positions[k + 1]], close_tuples)\n",
     "\n",
     "    #\n",
     "    # solution and search\n",
@@ -238,12 +266,10 @@
     "                val = solver.Value(var)\n",
     "                x = val % c\n",
     "                y = val // c\n",
-    "                color = 'white' if puzzle[y][x] == 0 else 'lightgreen'\n",
-    "                output.AddRectangle(x, r - y - 1, 1, 1, color, 'black',\n",
-    "                                    str(i + 1))\n",
+    "                color = \"white\" if puzzle[y][x] == 0 else \"lightgreen\"\n",
+    "                output.AddRectangle(x, r - y - 1, 1, 1, color, \"black\", str(i + 1))\n",
     "\n",
-    "            output.AddTitle('Puzzle %i solved in %f s' %\n",
-    "                            (index, solver.WallTime()))\n",
+    "            output.AddTitle(\"Puzzle %i solved in %f s\" % (index, solver.WallTime()))\n",
     "            output.Display()\n",
     "        else:\n",
     "            print_solution(\n",
@@ -251,10 +277,10 @@
     "                r,\n",
     "                c,\n",
     "            )\n",
-    "            print('Statistics')\n",
-    "            print('  - conflicts : %i' % solver.NumConflicts())\n",
-    "            print('  - branches  : %i' % solver.NumBranches())\n",
-    "            print('  - wall time : %f s' % solver.WallTime())\n",
+    "            print(\"Statistics\")\n",
+    "            print(\"  - conflicts : %i\" % solver.NumConflicts())\n",
+    "            print(\"  - branches  : %i\" % solver.NumBranches())\n",
+    "            print(\"  - wall time : %f s\" % solver.WallTime())\n",
     "\n",
     "\n",
     "def main(_):\n",
diff --git a/examples/notebook/examples/integer_programming.ipynb b/examples/notebook/examples/integer_programming.ipynb
index 4a641552e1..31a472e137 100644
--- a/examples/notebook/examples/integer_programming.ipynb
+++ b/examples/notebook/examples/integer_programming.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Integer programming examples that show how to use the APIs.\n"
    ]
   },
@@ -83,12 +84,12 @@
    "outputs": [],
    "source": [
     "from ortools.linear_solver import pywraplp\n",
-    "from ortools.init import pywrapinit\n",
     "\n",
     "\n",
     "def Announce(solver, api_type):\n",
-    "    print('---- Integer programming example with ' + solver + ' (' + api_type +\n",
-    "          ') -----')\n",
+    "    print(\n",
+    "        \"---- Integer programming example with \" + solver + \" (\" + api_type + \") -----\"\n",
+    "    )\n",
     "\n",
     "\n",
     "def RunIntegerExampleNaturalLanguageAPI(optimization_problem_type):\n",
@@ -98,12 +99,12 @@
     "    if not solver:\n",
     "        return\n",
     "\n",
-    "    Announce(optimization_problem_type, 'natural language API')\n",
+    "    Announce(optimization_problem_type, \"natural language API\")\n",
     "\n",
     "    infinity = solver.infinity()\n",
     "    # x1 and x2 are integer non-negative variables.\n",
-    "    x1 = solver.IntVar(0.0, infinity, 'x1')\n",
-    "    x2 = solver.IntVar(0.0, infinity, 'x2')\n",
+    "    x1 = solver.IntVar(0.0, infinity, \"x1\")\n",
+    "    x2 = solver.IntVar(0.0, infinity, \"x2\")\n",
     "\n",
     "    solver.Minimize(x1 + 2 * x2)\n",
     "    solver.Add(3 * x1 + 2 * x2 >= 17)\n",
@@ -117,12 +118,12 @@
     "    if not solver:\n",
     "        return\n",
     "\n",
-    "    Announce(optimization_problem_type, 'C++ style API')\n",
+    "    Announce(optimization_problem_type, \"C++ style API\")\n",
     "\n",
     "    infinity = solver.infinity()\n",
     "    # x1 and x2 are integer non-negative variables.\n",
-    "    x1 = solver.IntVar(0.0, infinity, 'x1')\n",
-    "    x2 = solver.IntVar(0.0, infinity, 'x2')\n",
+    "    x1 = solver.IntVar(0.0, infinity, \"x1\")\n",
+    "    x2 = solver.IntVar(0.0, infinity, \"x2\")\n",
     "\n",
     "    # Minimize x1 + 2 * x2.\n",
     "    objective = solver.Objective()\n",
@@ -139,8 +140,8 @@
     "\n",
     "def SolveAndPrint(solver, variable_list):\n",
     "    \"\"\"Solve the problem and print the solution.\"\"\"\n",
-    "    print('Number of variables = %d' % solver.NumVariables())\n",
-    "    print('Number of constraints = %d' % solver.NumConstraints())\n",
+    "    print(\"Number of variables = %d\" % solver.NumVariables())\n",
+    "    print(\"Number of constraints = %d\" % solver.NumConstraints())\n",
     "\n",
     "    result_status = solver.Solve()\n",
     "\n",
@@ -151,33 +152,33 @@
     "    # GLOP_LINEAR_PROGRAMMING, verifying the solution is highly recommended!).\n",
     "    assert solver.VerifySolution(1e-7, True)\n",
     "\n",
-    "    print('Problem solved in %f milliseconds' % solver.wall_time())\n",
+    "    print(\"Problem solved in %f milliseconds\" % solver.wall_time())\n",
     "\n",
     "    # The objective value of the solution.\n",
-    "    print('Optimal objective value = %f' % solver.Objective().Value())\n",
+    "    print(\"Optimal objective value = %f\" % solver.Objective().Value())\n",
     "\n",
     "    # The value of each variable in the solution.\n",
     "    for variable in variable_list:\n",
-    "        print('%s = %f' % (variable.name(), variable.solution_value()))\n",
+    "        print(\"%s = %f\" % (variable.name(), variable.solution_value()))\n",
     "\n",
-    "    print('Advanced usage:')\n",
-    "    print('Problem solved in %d branch-and-bound nodes' % solver.nodes())\n",
+    "    print(\"Advanced usage:\")\n",
+    "    print(\"Problem solved in %d branch-and-bound nodes\" % solver.nodes())\n",
     "\n",
     "\n",
     "def RunAllIntegerExampleNaturalLanguageAPI():\n",
-    "    RunIntegerExampleNaturalLanguageAPI('GLPK')\n",
-    "    RunIntegerExampleNaturalLanguageAPI('CBC')\n",
-    "    RunIntegerExampleNaturalLanguageAPI('SCIP')\n",
-    "    RunIntegerExampleNaturalLanguageAPI('SAT')\n",
-    "    RunIntegerExampleNaturalLanguageAPI('Gurobi')\n",
+    "    RunIntegerExampleNaturalLanguageAPI(\"GLPK\")\n",
+    "    # Disabling due to ASAN errors with CBC.\n",
+    "    # RunIntegerExampleNaturalLanguageAPI('CBC')\n",
+    "    RunIntegerExampleNaturalLanguageAPI(\"SCIP\")\n",
+    "    RunIntegerExampleNaturalLanguageAPI(\"SAT\")\n",
     "\n",
     "\n",
     "def RunAllIntegerExampleCppStyleAPI():\n",
-    "    RunIntegerExampleCppStyleAPI('GLPK')\n",
-    "    RunIntegerExampleCppStyleAPI('CBC')\n",
-    "    RunIntegerExampleCppStyleAPI('SCIP')\n",
-    "    RunIntegerExampleCppStyleAPI('SAT')\n",
-    "    RunIntegerExampleCppStyleAPI('Gurobi')\n",
+    "    RunIntegerExampleCppStyleAPI(\"GLPK\")\n",
+    "    # Disabling due to ASAN errors with CBC.\n",
+    "    # RunIntegerExampleCppStyleAPI('CBC')\n",
+    "    RunIntegerExampleCppStyleAPI(\"SCIP\")\n",
+    "    RunIntegerExampleCppStyleAPI(\"SAT\")\n",
     "\n",
     "\n",
     "def main():\n",
@@ -185,11 +186,6 @@
     "    RunAllIntegerExampleCppStyleAPI()\n",
     "\n",
     "\n",
-    "pywrapinit.CppBridge.InitLogging('integer_programming.py')\n",
-    "cpp_flags = pywrapinit.CppFlags()\n",
-    "cpp_flags.stderrthreshold = 0\n",
-    "cpp_flags.log_prefix = False\n",
-    "pywrapinit.CppBridge.SetFlags(cpp_flags)\n",
     "main()\n",
     "\n"
    ]
diff --git a/examples/notebook/examples/jobshop_ft06_distance_sat.ipynb b/examples/notebook/examples/jobshop_ft06_distance_sat.ipynb
index 02398d10ce..950df8dedb 100644
--- a/examples/notebook/examples/jobshop_ft06_distance_sat.ipynb
+++ b/examples/notebook/examples/jobshop_ft06_distance_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "This model implements a variation of the ft06 jobshop.\n",
     "\n",
     "A jobshop is a standard scheduling problem when you must sequence a\n",
@@ -114,29 +115,42 @@
     "    all_machines = range(0, machines_count)\n",
     "    all_jobs = range(0, jobs_count)\n",
     "\n",
-    "    durations = [[1, 3, 6, 7, 3, 6], [8, 5, 10, 10, 10, 4], [5, 4, 8, 9, 1, 7],\n",
-    "                 [5, 5, 5, 3, 8, 9], [9, 3, 5, 4, 3, 1], [3, 3, 9, 10, 4, 1]]\n",
+    "    durations = [\n",
+    "        [1, 3, 6, 7, 3, 6],\n",
+    "        [8, 5, 10, 10, 10, 4],\n",
+    "        [5, 4, 8, 9, 1, 7],\n",
+    "        [5, 5, 5, 3, 8, 9],\n",
+    "        [9, 3, 5, 4, 3, 1],\n",
+    "        [3, 3, 9, 10, 4, 1],\n",
+    "    ]\n",
     "\n",
-    "    machines = [[2, 0, 1, 3, 5, 4], [1, 2, 4, 5, 0, 3], [2, 3, 5, 0, 1, 4],\n",
-    "                [1, 0, 2, 3, 4, 5], [2, 1, 4, 5, 0, 3], [1, 3, 5, 0, 4, 2]]\n",
+    "    machines = [\n",
+    "        [2, 0, 1, 3, 5, 4],\n",
+    "        [1, 2, 4, 5, 0, 3],\n",
+    "        [2, 3, 5, 0, 1, 4],\n",
+    "        [1, 0, 2, 3, 4, 5],\n",
+    "        [2, 1, 4, 5, 0, 3],\n",
+    "        [1, 3, 5, 0, 4, 2],\n",
+    "    ]\n",
     "\n",
     "    # Computes horizon statically.\n",
     "    horizon = 150\n",
     "\n",
-    "    task_type = collections.namedtuple('task_type', 'start end interval')\n",
+    "    task_type = collections.namedtuple(\"task_type\", \"start end interval\")\n",
     "\n",
     "    # Creates jobs.\n",
     "    all_tasks = {}\n",
     "    for i in all_jobs:\n",
     "        for j in all_machines:\n",
-    "            start_var = model.NewIntVar(0, horizon, 'start_%i_%i' % (i, j))\n",
+    "            start_var = model.NewIntVar(0, horizon, \"start_%i_%i\" % (i, j))\n",
     "            duration = durations[i][j]\n",
-    "            end_var = model.NewIntVar(0, horizon, 'end_%i_%i' % (i, j))\n",
-    "            interval_var = model.NewIntervalVar(start_var, duration, end_var,\n",
-    "                                                'interval_%i_%i' % (i, j))\n",
-    "            all_tasks[(i, j)] = task_type(start=start_var,\n",
-    "                                          end=end_var,\n",
-    "                                          interval=interval_var)\n",
+    "            end_var = model.NewIntVar(0, horizon, \"end_%i_%i\" % (i, j))\n",
+    "            interval_var = model.NewIntervalVar(\n",
+    "                start_var, duration, end_var, \"interval_%i_%i\" % (i, j)\n",
+    "            )\n",
+    "            all_tasks[(i, j)] = task_type(\n",
+    "                start=start_var, end=end_var, interval=interval_var\n",
+    "            )\n",
     "\n",
     "    # Create disjuctive constraints.\n",
     "    for i in all_machines:\n",
@@ -156,23 +170,24 @@
     "        arcs = []\n",
     "        for j1 in range(len(job_intervals)):\n",
     "            # Initial arc from the dummy node (0) to a task.\n",
-    "            start_lit = model.NewBoolVar('%i is first job' % j1)\n",
-    "            arcs.append([0, j1 + 1, start_lit])\n",
+    "            start_lit = model.NewBoolVar(\"%i is first job\" % j1)\n",
+    "            arcs.append((0, j1 + 1, start_lit))\n",
     "            # Final arc from an arc to the dummy node.\n",
-    "            arcs.append([j1 + 1, 0, model.NewBoolVar('%i is last job' % j1)])\n",
+    "            arcs.append((j1 + 1, 0, model.NewBoolVar(\"%i is last job\" % j1)))\n",
     "\n",
     "            for j2 in range(len(job_intervals)):\n",
     "                if j1 == j2:\n",
     "                    continue\n",
     "\n",
-    "                lit = model.NewBoolVar('%i follows %i' % (j2, j1))\n",
-    "                arcs.append([j1 + 1, j2 + 1, lit])\n",
+    "                lit = model.NewBoolVar(\"%i follows %i\" % (j2, j1))\n",
+    "                arcs.append((j1 + 1, j2 + 1, lit))\n",
     "\n",
     "                # We add the reified precedence to link the literal with the\n",
     "                # times of the two tasks.\n",
     "                min_distance = distance_between_jobs(j1, j2)\n",
-    "                model.Add(job_starts[j2] >= job_ends[j1] +\n",
-    "                          min_distance).OnlyEnforceIf(lit)\n",
+    "                model.Add(job_starts[j2] >= job_ends[j1] + min_distance).OnlyEnforceIf(\n",
+    "                    lit\n",
+    "                )\n",
     "\n",
     "        model.AddCircuit(arcs)\n",
     "\n",
@@ -182,9 +197,10 @@
     "            model.Add(all_tasks[(i, j + 1)].start >= all_tasks[(i, j)].end)\n",
     "\n",
     "    # Makespan objective.\n",
-    "    obj_var = model.NewIntVar(0, horizon, 'makespan')\n",
+    "    obj_var = model.NewIntVar(0, horizon, \"makespan\")\n",
     "    model.AddMaxEquality(\n",
-    "        obj_var, [all_tasks[(i, machines_count - 1)].end for i in all_jobs])\n",
+    "        obj_var, [all_tasks[(i, machines_count - 1)].end for i in all_jobs]\n",
+    "    )\n",
     "    model.Minimize(obj_var)\n",
     "\n",
     "    # Solve model.\n",
@@ -193,7 +209,7 @@
     "\n",
     "    # Output solution.\n",
     "    if status == cp_model.OPTIMAL:\n",
-    "        print('Optimal makespan: %i' % solver.ObjectiveValue())\n",
+    "        print(\"Optimal makespan: %i\" % solver.ObjectiveValue())\n",
     "\n",
     "\n",
     "jobshop_ft06_distance()\n",
diff --git a/examples/notebook/examples/jobshop_ft06_sat.ipynb b/examples/notebook/examples/jobshop_ft06_sat.ipynb
index 8ac074f9ff..cc11ef1ea2 100644
--- a/examples/notebook/examples/jobshop_ft06_sat.ipynb
+++ b/examples/notebook/examples/jobshop_ft06_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "This model implements a simple jobshop named ft06.\n",
     "\n",
     "A jobshop is a standard scheduling problem when you must sequence a\n",
@@ -107,29 +108,42 @@
     "    all_machines = range(0, machines_count)\n",
     "    all_jobs = range(0, jobs_count)\n",
     "\n",
-    "    durations = [[1, 3, 6, 7, 3, 6], [8, 5, 10, 10, 10, 4], [5, 4, 8, 9, 1, 7],\n",
-    "                 [5, 5, 5, 3, 8, 9], [9, 3, 5, 4, 3, 1], [3, 3, 9, 10, 4, 1]]\n",
+    "    durations = [\n",
+    "        [1, 3, 6, 7, 3, 6],\n",
+    "        [8, 5, 10, 10, 10, 4],\n",
+    "        [5, 4, 8, 9, 1, 7],\n",
+    "        [5, 5, 5, 3, 8, 9],\n",
+    "        [9, 3, 5, 4, 3, 1],\n",
+    "        [3, 3, 9, 10, 4, 1],\n",
+    "    ]\n",
     "\n",
-    "    machines = [[2, 0, 1, 3, 5, 4], [1, 2, 4, 5, 0, 3], [2, 3, 5, 0, 1, 4],\n",
-    "                [1, 0, 2, 3, 4, 5], [2, 1, 4, 5, 0, 3], [1, 3, 5, 0, 4, 2]]\n",
+    "    machines = [\n",
+    "        [2, 0, 1, 3, 5, 4],\n",
+    "        [1, 2, 4, 5, 0, 3],\n",
+    "        [2, 3, 5, 0, 1, 4],\n",
+    "        [1, 0, 2, 3, 4, 5],\n",
+    "        [2, 1, 4, 5, 0, 3],\n",
+    "        [1, 3, 5, 0, 4, 2],\n",
+    "    ]\n",
     "\n",
     "    # Computes horizon dynamically.\n",
     "    horizon = sum([sum(durations[i]) for i in all_jobs])\n",
     "\n",
-    "    task_type = collections.namedtuple('task_type', 'start end interval')\n",
+    "    task_type = collections.namedtuple(\"task_type\", \"start end interval\")\n",
     "\n",
     "    # Creates jobs.\n",
     "    all_tasks = {}\n",
     "    for i in all_jobs:\n",
     "        for j in all_machines:\n",
-    "            start_var = model.NewIntVar(0, horizon, 'start_%i_%i' % (i, j))\n",
+    "            start_var = model.NewIntVar(0, horizon, \"start_%i_%i\" % (i, j))\n",
     "            duration = durations[i][j]\n",
-    "            end_var = model.NewIntVar(0, horizon, 'end_%i_%i' % (i, j))\n",
-    "            interval_var = model.NewIntervalVar(start_var, duration, end_var,\n",
-    "                                                'interval_%i_%i' % (i, j))\n",
-    "            all_tasks[(i, j)] = task_type(start=start_var,\n",
-    "                                          end=end_var,\n",
-    "                                          interval=interval_var)\n",
+    "            end_var = model.NewIntVar(0, horizon, \"end_%i_%i\" % (i, j))\n",
+    "            interval_var = model.NewIntervalVar(\n",
+    "                start_var, duration, end_var, \"interval_%i_%i\" % (i, j)\n",
+    "            )\n",
+    "            all_tasks[(i, j)] = task_type(\n",
+    "                start=start_var, end=end_var, interval=interval_var\n",
+    "            )\n",
     "\n",
     "    # Create disjuctive constraints.\n",
     "    machine_to_jobs = {}\n",
@@ -148,9 +162,10 @@
     "            model.Add(all_tasks[(i, j + 1)].start >= all_tasks[(i, j)].end)\n",
     "\n",
     "    # Makespan objective.\n",
-    "    obj_var = model.NewIntVar(0, horizon, 'makespan')\n",
+    "    obj_var = model.NewIntVar(0, horizon, \"makespan\")\n",
     "    model.AddMaxEquality(\n",
-    "        obj_var, [all_tasks[(i, machines_count - 1)].end for i in all_jobs])\n",
+    "        obj_var, [all_tasks[(i, machines_count - 1)].end for i in all_jobs]\n",
+    "    )\n",
     "    model.Minimize(obj_var)\n",
     "\n",
     "    # Solve model.\n",
@@ -161,12 +176,13 @@
     "    # Output solution.\n",
     "    if status == cp_model.OPTIMAL:\n",
     "        if visualization.RunFromIPython():\n",
-    "            starts = [[\n",
-    "                solver.Value(all_tasks[(i, j)][0]) for j in all_machines\n",
-    "            ] for i in all_jobs]\n",
-    "            visualization.DisplayJobshop(starts, durations, machines, 'FT06')\n",
+    "            starts = [\n",
+    "                [solver.Value(all_tasks[(i, j)][0]) for j in all_machines]\n",
+    "                for i in all_jobs\n",
+    "            ]\n",
+    "            visualization.DisplayJobshop(starts, durations, machines, \"FT06\")\n",
     "        else:\n",
-    "            print('Optimal makespan: %i' % solver.ObjectiveValue())\n",
+    "            print(\"Optimal makespan: %i\" % solver.ObjectiveValue())\n",
     "\n",
     "\n",
     "jobshop_ft06()\n",
diff --git a/examples/notebook/examples/jobshop_with_maintenance_sat.ipynb b/examples/notebook/examples/jobshop_with_maintenance_sat.ipynb
index 3663569250..3a858dd6ae 100644
--- a/examples/notebook/examples/jobshop_with_maintenance_sat.ipynb
+++ b/examples/notebook/examples/jobshop_with_maintenance_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Jobshop with maintenance tasks using the CP-SAT solver.\n"
    ]
   },
@@ -96,8 +97,10 @@
     "\n",
     "    def on_solution_callback(self):\n",
     "        \"\"\"Called at each new solution.\"\"\"\n",
-    "        print('Solution %i, time = %f s, objective = %i' %\n",
-    "              (self.__solution_count, self.WallTime(), self.ObjectiveValue()))\n",
+    "        print(\n",
+    "            \"Solution %i, time = %f s, objective = %i\"\n",
+    "            % (self.__solution_count, self.WallTime(), self.ObjectiveValue())\n",
+    "        )\n",
     "        self.__solution_count += 1\n",
     "\n",
     "\n",
@@ -119,10 +122,11 @@
     "    horizon = sum(task[1] for job in jobs_data for task in job)\n",
     "\n",
     "    # Named tuple to store information about created variables.\n",
-    "    task_type = collections.namedtuple('Task', 'start end interval')\n",
+    "    task_type = collections.namedtuple(\"Task\", \"start end interval\")\n",
     "    # Named tuple to manipulate solution information.\n",
-    "    assigned_task_type = collections.namedtuple('assigned_task_type',\n",
-    "                                                'start job index duration')\n",
+    "    assigned_task_type = collections.namedtuple(\n",
+    "        \"assigned_task_type\", \"start job index duration\"\n",
+    "    )\n",
     "\n",
     "    # Creates job intervals and add to the corresponding machine lists.\n",
     "    all_tasks = {}\n",
@@ -132,18 +136,19 @@
     "        for task_id, task in enumerate(job):\n",
     "            machine = task[0]\n",
     "            duration = task[1]\n",
-    "            suffix = '_%i_%i' % (job_id, task_id)\n",
-    "            start_var = model.NewIntVar(0, horizon, 'start' + suffix)\n",
-    "            end_var = model.NewIntVar(0, horizon, 'end' + suffix)\n",
-    "            interval_var = model.NewIntervalVar(start_var, duration, end_var,\n",
-    "                                                'interval' + suffix)\n",
-    "            all_tasks[job_id, task_id] = task_type(start=start_var,\n",
-    "                                                   end=end_var,\n",
-    "                                                   interval=interval_var)\n",
+    "            suffix = \"_%i_%i\" % (job_id, task_id)\n",
+    "            start_var = model.NewIntVar(0, horizon, \"start\" + suffix)\n",
+    "            end_var = model.NewIntVar(0, horizon, \"end\" + suffix)\n",
+    "            interval_var = model.NewIntervalVar(\n",
+    "                start_var, duration, end_var, \"interval\" + suffix\n",
+    "            )\n",
+    "            all_tasks[job_id, task_id] = task_type(\n",
+    "                start=start_var, end=end_var, interval=interval_var\n",
+    "            )\n",
     "            machine_to_intervals[machine].append(interval_var)\n",
     "\n",
     "    # Add maintenance interval (machine 0 is not available on time {4, 5, 6, 7}).\n",
-    "    machine_to_intervals[0].append(model.NewIntervalVar(4, 4, 8, 'weekend_0'))\n",
+    "    machine_to_intervals[0].append(model.NewIntervalVar(4, 4, 8, \"weekend_0\"))\n",
     "\n",
     "    # Create and add disjunctive constraints.\n",
     "    for machine in all_machines:\n",
@@ -152,15 +157,16 @@
     "    # Precedences inside a job.\n",
     "    for job_id, job in enumerate(jobs_data):\n",
     "        for task_id in range(len(job) - 1):\n",
-    "            model.Add(all_tasks[job_id, task_id +\n",
-    "                                1].start >= all_tasks[job_id, task_id].end)\n",
+    "            model.Add(\n",
+    "                all_tasks[job_id, task_id + 1].start >= all_tasks[job_id, task_id].end\n",
+    "            )\n",
     "\n",
     "    # Makespan objective.\n",
-    "    obj_var = model.NewIntVar(0, horizon, 'makespan')\n",
-    "    model.AddMaxEquality(obj_var, [\n",
-    "        all_tasks[job_id, len(job) - 1].end\n",
-    "        for job_id, job in enumerate(jobs_data)\n",
-    "    ])\n",
+    "    obj_var = model.NewIntVar(0, horizon, \"makespan\")\n",
+    "    model.AddMaxEquality(\n",
+    "        obj_var,\n",
+    "        [all_tasks[job_id, len(job) - 1].end for job_id, job in enumerate(jobs_data)],\n",
+    "    )\n",
     "    model.Minimize(obj_var)\n",
     "\n",
     "    # Solve model.\n",
@@ -176,48 +182,50 @@
     "            for task_id, task in enumerate(job):\n",
     "                machine = task[0]\n",
     "                assigned_jobs[machine].append(\n",
-    "                    assigned_task_type(start=solver.Value(\n",
-    "                        all_tasks[job_id, task_id].start),\n",
-    "                                       job=job_id,\n",
-    "                                       index=task_id,\n",
-    "                                       duration=task[1]))\n",
+    "                    assigned_task_type(\n",
+    "                        start=solver.Value(all_tasks[job_id, task_id].start),\n",
+    "                        job=job_id,\n",
+    "                        index=task_id,\n",
+    "                        duration=task[1],\n",
+    "                    )\n",
+    "                )\n",
     "\n",
     "        # Create per machine output lines.\n",
-    "        output = ''\n",
+    "        output = \"\"\n",
     "        for machine in all_machines:\n",
     "            # Sort by starting time.\n",
     "            assigned_jobs[machine].sort()\n",
-    "            sol_line_tasks = 'Machine ' + str(machine) + ': '\n",
-    "            sol_line = '           '\n",
+    "            sol_line_tasks = \"Machine \" + str(machine) + \": \"\n",
+    "            sol_line = \"           \"\n",
     "\n",
     "            for assigned_task in assigned_jobs[machine]:\n",
-    "                name = 'job_%i_%i' % (assigned_task.job, assigned_task.index)\n",
+    "                name = \"job_%i_%i\" % (assigned_task.job, assigned_task.index)\n",
     "                # Add spaces to output to align columns.\n",
-    "                sol_line_tasks += '%-10s' % name\n",
+    "                sol_line_tasks += \"%-10s\" % name\n",
     "                start = assigned_task.start\n",
     "                duration = assigned_task.duration\n",
     "\n",
-    "                sol_tmp = '[%i,%i]' % (start, start + duration)\n",
+    "                sol_tmp = \"[%i,%i]\" % (start, start + duration)\n",
     "                # Add spaces to output to align columns.\n",
-    "                sol_line += '%-10s' % sol_tmp\n",
+    "                sol_line += \"%-10s\" % sol_tmp\n",
     "\n",
-    "            sol_line += '\\n'\n",
-    "            sol_line_tasks += '\\n'\n",
+    "            sol_line += \"\\n\"\n",
+    "            sol_line_tasks += \"\\n\"\n",
     "            output += sol_line_tasks\n",
     "            output += sol_line\n",
     "\n",
     "        # Finally print the solution found.\n",
-    "        print('Optimal Schedule Length: %i' % solver.ObjectiveValue())\n",
+    "        print(\"Optimal Schedule Length: %i\" % solver.ObjectiveValue())\n",
     "        print(output)\n",
-    "        print('Statistics')\n",
-    "        print('  - conflicts : %i' % solver.NumConflicts())\n",
-    "        print('  - branches  : %i' % solver.NumBranches())\n",
-    "        print('  - wall time : %f s' % solver.WallTime())\n",
+    "        print(\"Statistics\")\n",
+    "        print(\"  - conflicts : %i\" % solver.NumConflicts())\n",
+    "        print(\"  - branches  : %i\" % solver.NumBranches())\n",
+    "        print(\"  - wall time : %f s\" % solver.WallTime())\n",
     "\n",
     "\n",
     "def main(argv: Sequence[str]) -> None:\n",
     "    if len(argv) > 1:\n",
-    "        raise app.UsageError('Too many command-line arguments.')\n",
+    "        raise app.UsageError(\"Too many command-line arguments.\")\n",
     "    jobshop_with_maintenance()\n",
     "\n",
     "\n",
diff --git a/examples/notebook/examples/knapsack_2d_sat.ipynb b/examples/notebook/examples/knapsack_2d_sat.ipynb
index 008d034df7..75dc9c3224 100644
--- a/examples/notebook/examples/knapsack_2d_sat.ipynb
+++ b/examples/notebook/examples/knapsack_2d_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Solver a 2D rectangle knapsack problem.\n",
     "\n",
     "This code is adapted from\n",
@@ -96,14 +97,16 @@
     "from ortools.sat.python import cp_model\n",
     "\n",
     "_OUTPUT_PROTO = flags.DEFINE_string(\n",
-    "    'output_proto', '', 'Output file to write the cp_model proto to.')\n",
-    "_PARAMS = flags.DEFINE_string(\n",
-    "    'params',\n",
-    "    'num_search_workers:16,log_search_progress:true,max_time_in_seconds:45',\n",
-    "    'Sat solver parameters.',\n",
+    "    \"output_proto\", \"\", \"Output file to write the cp_model proto to.\"\n",
+    ")\n",
+    "_PARAMS = flags.DEFINE_string(\n",
+    "    \"params\",\n",
+    "    \"num_search_workers:16,log_search_progress:true,max_time_in_seconds:45\",\n",
+    "    \"Sat solver parameters.\",\n",
+    ")\n",
+    "_MODEL = flags.DEFINE_string(\n",
+    "    \"model\", \"rotation\", \"'duplicate' or 'rotation' or 'optional'\"\n",
     ")\n",
-    "_MODEL = flags.DEFINE_string('model', 'rotation',\n",
-    "                             '\\'duplicate\\' or \\'rotation\\' or \\'optional\\'')\n",
     "\n",
     "\n",
     "def build_data():\n",
@@ -122,25 +125,25 @@
     "    k10             9      11       5        369.560   cyan\n",
     "    \"\"\"\n",
     "\n",
-    "    data = pd.read_table(io.StringIO(data), sep=r'\\s+')\n",
-    "    print('Input data')\n",
+    "    data = pd.read_table(io.StringIO(data), sep=r\"\\s+\")\n",
+    "    print(\"Input data\")\n",
     "    print(data)\n",
     "\n",
     "    max_height = 20\n",
     "    max_width = 30\n",
     "\n",
-    "    print(f'Container max_width:{max_width} max_height:{max_height}')\n",
-    "    print(f'#Items: {len(data.index)}')\n",
+    "    print(f\"Container max_width:{max_width} max_height:{max_height}\")\n",
+    "    print(f\"#Items: {len(data.index)}\")\n",
     "    return (data, max_height, max_width)\n",
     "\n",
     "\n",
     "def solve_with_duplicate_items(data, max_height, max_width):\n",
     "    \"\"\"Solve the problem by building 2 items (rotated or not) for each item.\"\"\"\n",
     "    # Derived data (expanded to individual items).\n",
-    "    data_widths = data['width'].to_numpy()\n",
-    "    data_heights = data['height'].to_numpy()\n",
-    "    data_availability = data['available'].to_numpy()\n",
-    "    data_values = data['value'].to_numpy()\n",
+    "    data_widths = data[\"width\"].to_numpy()\n",
+    "    data_heights = data[\"height\"].to_numpy()\n",
+    "    data_availability = data[\"available\"].to_numpy()\n",
+    "    data_values = data[\"value\"].to_numpy()\n",
     "\n",
     "    # Non duplicated items data.\n",
     "    base_item_widths = np.repeat(data_widths, data_availability)\n",
@@ -169,21 +172,29 @@
     "\n",
     "    for i in range(num_items):\n",
     "        ## Is the item used?\n",
-    "        is_used.append(model.NewBoolVar(f'is_used{i}'))\n",
+    "        is_used.append(model.NewBoolVar(f\"is_used{i}\"))\n",
     "\n",
     "        ## Item coordinates.\n",
-    "        x_starts.append(model.NewIntVar(0, max_width, f'x_start{i}'))\n",
-    "        x_ends.append(model.NewIntVar(0, max_width, f'x_end{i}'))\n",
-    "        y_starts.append(model.NewIntVar(0, max_height, f'y_start{i}'))\n",
-    "        y_ends.append(model.NewIntVar(0, max_height, f'y_end{i}'))\n",
+    "        x_starts.append(model.NewIntVar(0, max_width, f\"x_start{i}\"))\n",
+    "        x_ends.append(model.NewIntVar(0, max_width, f\"x_end{i}\"))\n",
+    "        y_starts.append(model.NewIntVar(0, max_height, f\"y_start{i}\"))\n",
+    "        y_ends.append(model.NewIntVar(0, max_height, f\"y_end{i}\"))\n",
     "\n",
     "        ## Interval variables.\n",
     "        x_intervals.append(\n",
-    "            model.NewIntervalVar(x_starts[i], item_widths[i] * is_used[i],\n",
-    "                                 x_ends[i], f'x_interval{i}'))\n",
+    "            model.NewIntervalVar(\n",
+    "                x_starts[i], item_widths[i] * is_used[i], x_ends[i], f\"x_interval{i}\"\n",
+    "            )\n",
+    "        )\n",
     "        y_intervals.append(\n",
-    "            model.NewIntervalVar(y_starts[i], item_heights[i] * is_used[i],\n",
-    "                                 y_ends[i], f'y_interval{i}'))\n",
+    "            model.NewIntervalVar(\n",
+    "                y_starts[i], item_heights[i] * is_used[i], y_ends[i], f\"y_interval{i}\"\n",
+    "            )\n",
+    "        )\n",
+    "\n",
+    "        # Unused boxes are fixed at (0.0).\n",
+    "        model.Add(x_starts[i] == 0).OnlyEnforceIf(is_used[i].Not())\n",
+    "        model.Add(y_starts[i] == 0).OnlyEnforceIf(is_used[i].Not())\n",
     "\n",
     "    # Constraints.\n",
     "\n",
@@ -199,8 +210,8 @@
     "\n",
     "    # Output proto to file.\n",
     "    if _OUTPUT_PROTO.value:\n",
-    "        print('Writing proto to %s' % _OUTPUT_PROTO.value)\n",
-    "        with open(_OUTPUT_PROTO.value, 'w') as text_file:\n",
+    "        print(f\"Writing proto to {_OUTPUT_PROTO.value}\")\n",
+    "        with open(_OUTPUT_PROTO.value, \"w\") as text_file:\n",
     "            text_file.write(str(model))\n",
     "\n",
     "    # Solve model.\n",
@@ -211,27 +222,29 @@
     "    status = solver.Solve(model)\n",
     "\n",
     "    # Report solution.\n",
-    "    if status == cp_model.OPTIMAL:\n",
+    "    if status == cp_model.OPTIMAL or status == cp_model.FEASIBLE:\n",
     "        used = {i for i in range(num_items) if solver.BooleanValue(is_used[i])}\n",
-    "        data = pd.DataFrame({\n",
-    "            'x_start': [solver.Value(x_starts[i]) for i in used],\n",
-    "            'y_start': [solver.Value(y_starts[i]) for i in used],\n",
-    "            'item_width': [item_widths[i] for i in used],\n",
-    "            'item_height': [item_heights[i] for i in used],\n",
-    "            'x_end': [solver.Value(x_ends[i]) for i in used],\n",
-    "            'y_end': [solver.Value(y_ends[i]) for i in used],\n",
-    "            'item_value': [item_values[i] for i in used]\n",
-    "        })\n",
+    "        data = pd.DataFrame(\n",
+    "            {\n",
+    "                \"x_start\": [solver.Value(x_starts[i]) for i in used],\n",
+    "                \"y_start\": [solver.Value(y_starts[i]) for i in used],\n",
+    "                \"item_width\": [item_widths[i] for i in used],\n",
+    "                \"item_height\": [item_heights[i] for i in used],\n",
+    "                \"x_end\": [solver.Value(x_ends[i]) for i in used],\n",
+    "                \"y_end\": [solver.Value(y_ends[i]) for i in used],\n",
+    "                \"item_value\": [item_values[i] for i in used],\n",
+    "            }\n",
+    "        )\n",
     "        print(data)\n",
     "\n",
     "\n",
     "def solve_with_duplicate_optional_items(data, max_height, max_width):\n",
     "    \"\"\"Solve the problem by building 2 optional items (rotated or not) for each item.\"\"\"\n",
     "    # Derived data (expanded to individual items).\n",
-    "    data_widths = data['width'].to_numpy()\n",
-    "    data_heights = data['height'].to_numpy()\n",
-    "    data_availability = data['available'].to_numpy()\n",
-    "    data_values = data['value'].to_numpy()\n",
+    "    data_widths = data[\"width\"].to_numpy()\n",
+    "    data_heights = data[\"height\"].to_numpy()\n",
+    "    data_availability = data[\"available\"].to_numpy()\n",
+    "    data_values = data[\"value\"].to_numpy()\n",
     "\n",
     "    # Non duplicated items data.\n",
     "    base_item_widths = np.repeat(data_widths, data_availability)\n",
@@ -258,22 +271,30 @@
     "\n",
     "    for i in range(num_items):\n",
     "        ## Is the item used?\n",
-    "        is_used.append(model.NewBoolVar(f'is_used{i}'))\n",
+    "        is_used.append(model.NewBoolVar(f\"is_used{i}\"))\n",
     "\n",
     "        ## Item coordinates.\n",
     "        x_starts.append(\n",
-    "            model.NewIntVar(0, max_width - int(item_widths[i]), f'x_start{i}'))\n",
+    "            model.NewIntVar(0, max_width - int(item_widths[i]), f\"x_start{i}\")\n",
+    "        )\n",
     "        y_starts.append(\n",
-    "            model.NewIntVar(0, max_height - int(item_heights[i]),\n",
-    "                            f'y_start{i}'))\n",
+    "            model.NewIntVar(0, max_height - int(item_heights[i]), f\"y_start{i}\")\n",
+    "        )\n",
     "\n",
     "        ## Interval variables.\n",
     "        x_intervals.append(\n",
-    "            model.NewOptionalFixedSizeIntervalVar(x_starts[i], item_widths[i],\n",
-    "                                                  is_used[i], f'x_interval{i}'))\n",
+    "            model.NewOptionalFixedSizeIntervalVar(\n",
+    "                x_starts[i], item_widths[i], is_used[i], f\"x_interval{i}\"\n",
+    "            )\n",
+    "        )\n",
     "        y_intervals.append(\n",
-    "            model.NewOptionalFixedSizeIntervalVar(y_starts[i], item_heights[i],\n",
-    "                                                  is_used[i], f'y_interval{i}'))\n",
+    "            model.NewOptionalFixedSizeIntervalVar(\n",
+    "                y_starts[i], item_heights[i], is_used[i], f\"y_interval{i}\"\n",
+    "            )\n",
+    "        )\n",
+    "        # Unused boxes are fixed at (0.0).\n",
+    "        model.Add(x_starts[i] == 0).OnlyEnforceIf(is_used[i].Not())\n",
+    "        model.Add(y_starts[i] == 0).OnlyEnforceIf(is_used[i].Not())\n",
     "\n",
     "    # Constraints.\n",
     "\n",
@@ -289,8 +310,8 @@
     "\n",
     "    # Output proto to file.\n",
     "    if _OUTPUT_PROTO.value:\n",
-    "        print('Writing proto to %s' % _OUTPUT_PROTO.value)\n",
-    "        with open(_OUTPUT_PROTO.value, 'w') as text_file:\n",
+    "        print(f\"Writing proto to {_OUTPUT_PROTO.value}\")\n",
+    "        with open(_OUTPUT_PROTO.value, \"w\") as text_file:\n",
     "            text_file.write(str(model))\n",
     "\n",
     "    # Solve model.\n",
@@ -301,29 +322,29 @@
     "    status = solver.Solve(model)\n",
     "\n",
     "    # Report solution.\n",
-    "    if status == cp_model.OPTIMAL:\n",
+    "    if status == cp_model.OPTIMAL or status == cp_model.FEASIBLE:\n",
     "        used = {i for i in range(num_items) if solver.BooleanValue(is_used[i])}\n",
-    "        data = pd.DataFrame({\n",
-    "            'x_start': [solver.Value(x_starts[i]) for i in used],\n",
-    "            'y_start': [solver.Value(y_starts[i]) for i in used],\n",
-    "            'item_width': [item_widths[i] for i in used],\n",
-    "            'item_height': [item_heights[i] for i in used],\n",
-    "            'x_end': [solver.Value(x_starts[i]) + item_widths[i] for i in used],\n",
-    "            'y_end': [\n",
-    "                solver.Value(y_starts[i]) + item_heights[i] for i in used\n",
-    "            ],\n",
-    "            'item_value': [item_values[i] for i in used]\n",
-    "        })\n",
+    "        data = pd.DataFrame(\n",
+    "            {\n",
+    "                \"x_start\": [solver.Value(x_starts[i]) for i in used],\n",
+    "                \"y_start\": [solver.Value(y_starts[i]) for i in used],\n",
+    "                \"item_width\": [item_widths[i] for i in used],\n",
+    "                \"item_height\": [item_heights[i] for i in used],\n",
+    "                \"x_end\": [solver.Value(x_starts[i]) + item_widths[i] for i in used],\n",
+    "                \"y_end\": [solver.Value(y_starts[i]) + item_heights[i] for i in used],\n",
+    "                \"item_value\": [item_values[i] for i in used],\n",
+    "            }\n",
+    "        )\n",
     "        print(data)\n",
     "\n",
     "\n",
     "def solve_with_rotations(data, max_height, max_width):\n",
     "    \"\"\"Solve the problem by rotating items.\"\"\"\n",
     "    # Derived data (expanded to individual items).\n",
-    "    data_widths = data['width'].to_numpy()\n",
-    "    data_heights = data['height'].to_numpy()\n",
-    "    data_availability = data['available'].to_numpy()\n",
-    "    data_values = data['value'].to_numpy()\n",
+    "    data_widths = data[\"width\"].to_numpy()\n",
+    "    data_heights = data[\"height\"].to_numpy()\n",
+    "    data_availability = data[\"available\"].to_numpy()\n",
+    "    data_values = data[\"value\"].to_numpy()\n",
     "\n",
     "    item_widths = np.repeat(data_widths, data_availability)\n",
     "    item_heights = np.repeat(data_heights, data_availability)\n",
@@ -347,26 +368,26 @@
     "    for i in range(num_items):\n",
     "        sizes = [0, int(item_widths[i]), int(item_heights[i])]\n",
     "        # X coordinates.\n",
-    "        x_starts.append(model.NewIntVar(0, max_width, f'x_start{i}'))\n",
+    "        x_starts.append(model.NewIntVar(0, max_width, f\"x_start{i}\"))\n",
     "        x_sizes.append(\n",
-    "            model.NewIntVarFromDomain(cp_model.Domain.FromValues(sizes),\n",
-    "                                      f'x_size{i}'))\n",
-    "        x_ends.append(model.NewIntVar(0, max_width, f'x_end{i}'))\n",
+    "            model.NewIntVarFromDomain(cp_model.Domain.FromValues(sizes), f\"x_size{i}\")\n",
+    "        )\n",
+    "        x_ends.append(model.NewIntVar(0, max_width, f\"x_end{i}\"))\n",
     "\n",
     "        # Y coordinates.\n",
-    "        y_starts.append(model.NewIntVar(0, max_height, f'y_start{i}'))\n",
+    "        y_starts.append(model.NewIntVar(0, max_height, f\"y_start{i}\"))\n",
     "        y_sizes.append(\n",
-    "            model.NewIntVarFromDomain(cp_model.Domain.FromValues(sizes),\n",
-    "                                      f'y_size{i}'))\n",
-    "        y_ends.append(model.NewIntVar(0, max_height, f'y_end{i}'))\n",
+    "            model.NewIntVarFromDomain(cp_model.Domain.FromValues(sizes), f\"y_size{i}\")\n",
+    "        )\n",
+    "        y_ends.append(model.NewIntVar(0, max_height, f\"y_end{i}\"))\n",
     "\n",
     "        ## Interval variables\n",
     "        x_intervals.append(\n",
-    "            model.NewIntervalVar(x_starts[i], x_sizes[i], x_ends[i],\n",
-    "                                 f'x_interval{i}'))\n",
+    "            model.NewIntervalVar(x_starts[i], x_sizes[i], x_ends[i], f\"x_interval{i}\")\n",
+    "        )\n",
     "        y_intervals.append(\n",
-    "            model.NewIntervalVar(y_starts[i], y_sizes[i], y_ends[i],\n",
-    "                                 f'y_interval{i}'))\n",
+    "            model.NewIntervalVar(y_starts[i], y_sizes[i], y_ends[i], f\"y_interval{i}\")\n",
+    "        )\n",
     "\n",
     "    # is_used[i] == True if and only if item i is selected.\n",
     "    is_used = []\n",
@@ -375,9 +396,9 @@
     "\n",
     "    ## for each item, decide is unselected, no_rotation, rotated.\n",
     "    for i in range(num_items):\n",
-    "        not_selected = model.NewBoolVar(f'not_selected_{i}')\n",
-    "        no_rotation = model.NewBoolVar(f'no_rotation_{i}')\n",
-    "        rotated = model.NewBoolVar(f'rotated_{i}')\n",
+    "        not_selected = model.NewBoolVar(f\"not_selected_{i}\")\n",
+    "        no_rotation = model.NewBoolVar(f\"no_rotation_{i}\")\n",
+    "        rotated = model.NewBoolVar(f\"rotated_{i}\")\n",
     "\n",
     "        ### Exactly one state must be chosen.\n",
     "        model.AddExactlyOne(not_selected, no_rotation, rotated)\n",
@@ -385,8 +406,12 @@
     "        ### Define height and width according to the state.\n",
     "        dim1 = item_widths[i]\n",
     "        dim2 = item_heights[i]\n",
+    "        # Unused boxes are fixed at (0.0).\n",
     "        model.Add(x_sizes[i] == 0).OnlyEnforceIf(not_selected)\n",
     "        model.Add(y_sizes[i] == 0).OnlyEnforceIf(not_selected)\n",
+    "        model.Add(x_starts[i] == 0).OnlyEnforceIf(not_selected)\n",
+    "        model.Add(y_starts[i] == 0).OnlyEnforceIf(not_selected)\n",
+    "        # Sizes are fixed by the rotation.\n",
     "        model.Add(x_sizes[i] == dim1).OnlyEnforceIf(no_rotation)\n",
     "        model.Add(y_sizes[i] == dim2).OnlyEnforceIf(no_rotation)\n",
     "        model.Add(x_sizes[i] == dim2).OnlyEnforceIf(rotated)\n",
@@ -402,8 +427,8 @@
     "\n",
     "    # Output proto to file.\n",
     "    if _OUTPUT_PROTO.value:\n",
-    "        print('Writing proto to %s' % _OUTPUT_PROTO.value)\n",
-    "        with open(_OUTPUT_PROTO.value, 'w') as text_file:\n",
+    "        print(f\"Writing proto to {_OUTPUT_PROTO.value}\")\n",
+    "        with open(_OUTPUT_PROTO.value, \"w\") as text_file:\n",
     "            text_file.write(str(model))\n",
     "\n",
     "    # Solve model.\n",
@@ -414,26 +439,28 @@
     "    status = solver.Solve(model)\n",
     "\n",
     "    # Report solution.\n",
-    "    if status == cp_model.OPTIMAL:\n",
+    "    if status == cp_model.OPTIMAL or status == cp_model.FEASIBLE:\n",
     "        used = {i for i in range(num_items) if solver.BooleanValue(is_used[i])}\n",
-    "        data = pd.DataFrame({\n",
-    "            'x_start': [solver.Value(x_starts[i]) for i in used],\n",
-    "            'y_start': [solver.Value(y_starts[i]) for i in used],\n",
-    "            'item_width': [solver.Value(x_sizes[i]) for i in used],\n",
-    "            'item_height': [solver.Value(y_sizes[i]) for i in used],\n",
-    "            'x_end': [solver.Value(x_ends[i]) for i in used],\n",
-    "            'y_end': [solver.Value(y_ends[i]) for i in used],\n",
-    "            'item_value': [item_values[i] for i in used]\n",
-    "        })\n",
+    "        data = pd.DataFrame(\n",
+    "            {\n",
+    "                \"x_start\": [solver.Value(x_starts[i]) for i in used],\n",
+    "                \"y_start\": [solver.Value(y_starts[i]) for i in used],\n",
+    "                \"item_width\": [solver.Value(x_sizes[i]) for i in used],\n",
+    "                \"item_height\": [solver.Value(y_sizes[i]) for i in used],\n",
+    "                \"x_end\": [solver.Value(x_ends[i]) for i in used],\n",
+    "                \"y_end\": [solver.Value(y_ends[i]) for i in used],\n",
+    "                \"item_value\": [item_values[i] for i in used],\n",
+    "            }\n",
+    "        )\n",
     "        print(data)\n",
     "\n",
     "\n",
     "def main(_):\n",
     "    \"\"\"Solve the problem with all models.\"\"\"\n",
     "    data, max_height, max_width = build_data()\n",
-    "    if _MODEL.value == 'duplicate':\n",
+    "    if _MODEL.value == \"duplicate\":\n",
     "        solve_with_duplicate_items(data, max_height, max_width)\n",
-    "    elif _MODEL.value == 'optional':\n",
+    "    elif _MODEL.value == \"optional\":\n",
     "        solve_with_duplicate_optional_items(data, max_height, max_width)\n",
     "    else:\n",
     "        solve_with_rotations(data, max_height, max_width)\n",
diff --git a/examples/notebook/examples/line_balancing_sat.ipynb b/examples/notebook/examples/line_balancing_sat.ipynb
index b1ca8ec316..5473d88770 100644
--- a/examples/notebook/examples/line_balancing_sat.ipynb
+++ b/examples/notebook/examples/line_balancing_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Reader and solver of the single assembly line balancing problem.\n",
     "\n",
     "from https://assembly-line-balancing.de/salbp/:\n",
@@ -103,12 +104,14 @@
     "\n",
     "from ortools.sat.python import cp_model\n",
     "\n",
-    "_INPUT = flags.DEFINE_string('input', '', 'Input file to parse and solve.')\n",
-    "_PARAMS = flags.DEFINE_string('params', '', 'Sat solver parameters.')\n",
+    "_INPUT = flags.DEFINE_string(\"input\", \"\", \"Input file to parse and solve.\")\n",
+    "_PARAMS = flags.DEFINE_string(\"params\", \"\", \"Sat solver parameters.\")\n",
     "_OUTPUT_PROTO = flags.DEFINE_string(\n",
-    "    'output_proto', '', 'Output file to write the cp_model proto to.')\n",
-    "_MODEL = flags.DEFINE_string('model', 'boolean',\n",
-    "                             'Model used: boolean, scheduling, greedy')\n",
+    "    \"output_proto\", \"\", \"Output file to write the cp_model proto to.\"\n",
+    ")\n",
+    "_MODEL = flags.DEFINE_string(\n",
+    "    \"model\", \"boolean\", \"Model used: boolean, scheduling, greedy\"\n",
+    ")\n",
     "\n",
     "\n",
     "class SectionInfo(object):\n",
@@ -121,13 +124,13 @@
     "\n",
     "    def __str__(self):\n",
     "        if self.index_map:\n",
-    "            return f'SectionInfo(index_map={self.index_map})'\n",
+    "            return f\"SectionInfo(index_map={self.index_map})\"\n",
     "        elif self.set_of_pairs:\n",
-    "            return f'SectionInfo(set_of_pairs={self.set_of_pairs})'\n",
+    "            return f\"SectionInfo(set_of_pairs={self.set_of_pairs})\"\n",
     "        elif self.value is not None:\n",
-    "            return f'SectionInfo(value={self.value})'\n",
+    "            return f\"SectionInfo(value={self.value})\"\n",
     "        else:\n",
-    "            return 'SectionInfo()'\n",
+    "            return \"SectionInfo()\"\n",
     "\n",
     "\n",
     "def read_model(filename):\n",
@@ -136,64 +139,64 @@
     "    current_info = SectionInfo()\n",
     "\n",
     "    model = {}\n",
-    "    with open(filename, 'r') as input_file:\n",
-    "        print(f'Reading model from \\'{filename}\\'')\n",
-    "        section_name = ''\n",
+    "    with open(filename, \"r\") as input_file:\n",
+    "        print(f\"Reading model from '{filename}'\")\n",
+    "        section_name = \"\"\n",
     "\n",
     "        for line in input_file:\n",
     "            stripped_line = line.strip()\n",
     "            if not stripped_line:\n",
     "                continue\n",
     "\n",
-    "            match_section_def = re.match(r'<([\\w\\s]+)>', stripped_line)\n",
+    "            match_section_def = re.match(r\"<([\\w\\s]+)>\", stripped_line)\n",
     "            if match_section_def:\n",
     "                section_name = match_section_def.group(1)\n",
-    "                if section_name == 'end':\n",
+    "                if section_name == \"end\":\n",
     "                    continue\n",
     "\n",
     "                current_info = SectionInfo()\n",
     "                model[section_name] = current_info\n",
     "                continue\n",
     "\n",
-    "            match_single_number = re.match(r'^([0-9]+)$', stripped_line)\n",
+    "            match_single_number = re.match(r\"^([0-9]+)$\", stripped_line)\n",
     "            if match_single_number:\n",
     "                current_info.value = int(match_single_number.group(1))\n",
     "                continue\n",
     "\n",
-    "            match_key_value = re.match(r'^([0-9]+)\\s+([0-9]+)$', stripped_line)\n",
+    "            match_key_value = re.match(r\"^([0-9]+)\\s+([0-9]+)$\", stripped_line)\n",
     "            if match_key_value:\n",
     "                key = int(match_key_value.group(1))\n",
     "                value = int(match_key_value.group(2))\n",
     "                current_info.index_map[key] = value\n",
     "                continue\n",
     "\n",
-    "            match_pair = re.match(r'^([0-9]+),([0-9]+)$', stripped_line)\n",
+    "            match_pair = re.match(r\"^([0-9]+),([0-9]+)$\", stripped_line)\n",
     "            if match_pair:\n",
     "                left = int(match_pair.group(1))\n",
     "                right = int(match_pair.group(2))\n",
     "                current_info.set_of_pairs.add((left, right))\n",
     "                continue\n",
     "\n",
-    "            print(f'Unrecognized line \\'{stripped_line}\\'')\n",
+    "            print(f\"Unrecognized line '{stripped_line}'\")\n",
     "\n",
     "    return model\n",
     "\n",
     "\n",
     "def print_stats(model):\n",
-    "    print('Model Statistics')\n",
+    "    print(\"Model Statistics\")\n",
     "    for key, value in model.items():\n",
-    "        print(f'  - {key}: {value}')\n",
+    "        print(f\"  - {key}: {value}\")\n",
     "\n",
     "\n",
     "def solve_model_greedily(model):\n",
     "    \"\"\"Compute a greedy solution.\"\"\"\n",
-    "    print('Solving using a Greedy heuristics')\n",
+    "    print(\"Solving using a Greedy heuristics\")\n",
     "\n",
-    "    num_tasks = model['number of tasks'].value\n",
+    "    num_tasks = model[\"number of tasks\"].value\n",
     "    all_tasks = range(1, num_tasks + 1)  # Tasks are 1 based in the data.\n",
-    "    precedences = model['precedence relations'].set_of_pairs\n",
-    "    durations = model['task times'].index_map\n",
-    "    cycle_time = model['cycle time'].value\n",
+    "    precedences = model[\"precedence relations\"].set_of_pairs\n",
+    "    durations = model[\"task times\"].index_map\n",
+    "    cycle_time = model[\"cycle time\"].value\n",
     "\n",
     "    weights = collections.defaultdict(int)\n",
     "    successors = collections.defaultdict(list)\n",
@@ -212,7 +215,7 @@
     "\n",
     "    while len(assignment) < num_tasks:\n",
     "        if not candidates:\n",
-    "            print('error empty')\n",
+    "            print(\"error empty\")\n",
     "            break\n",
     "\n",
     "        best = -1\n",
@@ -242,7 +245,7 @@
     "                candidates.add(succ)\n",
     "                del weights[succ]\n",
     "\n",
-    "    print(f'  greedy solution uses {current_pod + 1} pods.')\n",
+    "    print(f\"  greedy solution uses {current_pod + 1} pods.\")\n",
     "\n",
     "    return assignment\n",
     "\n",
@@ -250,13 +253,13 @@
     "def solve_boolean_model(model, hint):\n",
     "    \"\"\"Solve the given model.\"\"\"\n",
     "\n",
-    "    print('Solving using the Boolean model')\n",
+    "    print(\"Solving using the Boolean model\")\n",
     "    # Model data\n",
-    "    num_tasks = model['number of tasks'].value\n",
+    "    num_tasks = model[\"number of tasks\"].value\n",
     "    all_tasks = range(1, num_tasks + 1)  # Tasks are 1 based in the model.\n",
-    "    durations = model['task times'].index_map\n",
-    "    precedences = model['precedence relations'].set_of_pairs\n",
-    "    cycle_time = model['cycle time'].value\n",
+    "    durations = model[\"task times\"].index_map\n",
+    "    precedences = model[\"precedence relations\"].set_of_pairs\n",
+    "    cycle_time = model[\"cycle time\"].value\n",
     "\n",
     "    num_pods = max(p for _, p in hint.items()) + 1 if hint else num_tasks - 1\n",
     "    all_pods = range(num_pods)\n",
@@ -271,11 +274,11 @@
     "    # Create the variables\n",
     "    for t in all_tasks:\n",
     "        for p in all_pods:\n",
-    "            assign[t, p] = model.NewBoolVar(f'assign_{t}_{p}')\n",
-    "            possible[t, p] = model.NewBoolVar(f'possible_{t}_{p}')\n",
+    "            assign[t, p] = model.NewBoolVar(f\"assign_{t}_{p}\")\n",
+    "            possible[t, p] = model.NewBoolVar(f\"possible_{t}_{p}\")\n",
     "\n",
     "    # active[p] indicates if pod p is active.\n",
-    "    active = [model.NewBoolVar(f'active_{p}') for p in all_pods]\n",
+    "    active = [model.NewBoolVar(f\"active_{p}\") for p in all_pods]\n",
     "\n",
     "    # Each task is done on exactly one pod.\n",
     "    for t in all_tasks:\n",
@@ -283,8 +286,7 @@
     "\n",
     "    # Total tasks assigned to one pod cannot exceed cycle time.\n",
     "    for p in all_pods:\n",
-    "        model.Add(\n",
-    "            sum(assign[t, p] * durations[t] for t in all_tasks) <= cycle_time)\n",
+    "        model.Add(sum(assign[t, p] * durations[t] for t in all_tasks) <= cycle_time)\n",
     "\n",
     "    # Maintain the possible variables:\n",
     "    #   possible at pod p -> possible at any pod after p\n",
@@ -302,8 +304,7 @@
     "    # Precedences.\n",
     "    for before, after in precedences:\n",
     "        for p in range(1, num_pods):\n",
-    "            model.AddImplication(assign[before, p], possible[after,\n",
-    "                                                             p - 1].Not())\n",
+    "            model.AddImplication(assign[before, p], possible[after, p - 1].Not())\n",
     "\n",
     "    # Link active variables with the assign one.\n",
     "    for p in all_pods:\n",
@@ -327,7 +328,7 @@
     "        model.AddHint(assign[t, hint[t]], 1)\n",
     "\n",
     "    if _OUTPUT_PROTO.value:\n",
-    "        print(f'Writing proto to {_OUTPUT_PROTO.value}')\n",
+    "        print(f\"Writing proto to {_OUTPUT_PROTO.value}\")\n",
     "        model.ExportToFile(_OUTPUT_PROTO.value)\n",
     "\n",
     "    # Solve model.\n",
@@ -341,13 +342,13 @@
     "def solve_scheduling_model(model, hint):\n",
     "    \"\"\"Solve the given model using a cumutive model.\"\"\"\n",
     "\n",
-    "    print('Solving using the scheduling model')\n",
+    "    print(\"Solving using the scheduling model\")\n",
     "    # Model data\n",
-    "    num_tasks = model['number of tasks'].value\n",
+    "    num_tasks = model[\"number of tasks\"].value\n",
     "    all_tasks = range(1, num_tasks + 1)  # Tasks are 1 based in the data.\n",
-    "    durations = model['task times'].index_map\n",
-    "    precedences = model['precedence relations'].set_of_pairs\n",
-    "    cycle_time = model['cycle time'].value\n",
+    "    durations = model[\"task times\"].index_map\n",
+    "    precedences = model[\"precedence relations\"].set_of_pairs\n",
+    "    cycle_time = model[\"cycle time\"].value\n",
     "\n",
     "    num_pods = max(p for _, p in hint.items()) + 1 if hint else num_tasks\n",
     "\n",
@@ -356,21 +357,20 @@
     "    # pod[t] indicates on which pod the task is performed.\n",
     "    pods = {}\n",
     "    for t in all_tasks:\n",
-    "        pods[t] = model.NewIntVar(0, num_pods - 1, f'pod_{t}')\n",
+    "        pods[t] = model.NewIntVar(0, num_pods - 1, f\"pod_{t}\")\n",
     "\n",
     "    # Create the variables\n",
     "    intervals = []\n",
     "    demands = []\n",
     "    for t in all_tasks:\n",
-    "        interval = model.NewFixedSizeIntervalVar(pods[t], 1, '')\n",
+    "        interval = model.NewFixedSizeIntervalVar(pods[t], 1, \"\")\n",
     "        intervals.append(interval)\n",
     "        demands.append(durations[t])\n",
     "\n",
     "    # Add terminating interval as the objective.\n",
-    "    obj_var = model.NewIntVar(1, num_pods, 'obj_var')\n",
-    "    obj_size = model.NewIntVar(1, num_pods, 'obj_duration')\n",
-    "    obj_interval = model.NewIntervalVar(obj_var, obj_size, num_pods + 1,\n",
-    "                                        'obj_interval')\n",
+    "    obj_var = model.NewIntVar(1, num_pods, \"obj_var\")\n",
+    "    obj_size = model.NewIntVar(1, num_pods, \"obj_duration\")\n",
+    "    obj_interval = model.NewIntervalVar(obj_var, obj_size, num_pods + 1, \"obj_interval\")\n",
     "    intervals.append(obj_interval)\n",
     "    demands.append(cycle_time)\n",
     "\n",
@@ -389,7 +389,7 @@
     "        model.AddHint(pods[t], hint[t])\n",
     "\n",
     "    if _OUTPUT_PROTO.value:\n",
-    "        print(f'Writing proto to{_OUTPUT_PROTO.value}')\n",
+    "        print(f\"Writing proto to{_OUTPUT_PROTO.value}\")\n",
     "        model.ExportToFile(_OUTPUT_PROTO.value)\n",
     "\n",
     "    # Solve model.\n",
@@ -402,15 +402,15 @@
     "\n",
     "def main(argv: Sequence[str]) -> None:\n",
     "    if len(argv) > 1:\n",
-    "        raise app.UsageError('Too many command-line arguments.')\n",
+    "        raise app.UsageError(\"Too many command-line arguments.\")\n",
     "\n",
     "    model = read_model(_INPUT.value)\n",
     "    print_stats(model)\n",
     "    greedy_solution = solve_model_greedily(model)\n",
     "\n",
-    "    if _MODEL.value == 'boolean':\n",
+    "    if _MODEL.value == \"boolean\":\n",
     "        solve_boolean_model(model, greedy_solution)\n",
-    "    elif _MODEL.value == 'scheduling':\n",
+    "    elif _MODEL.value == \"scheduling\":\n",
     "        solve_scheduling_model(model, greedy_solution)\n",
     "\n",
     "\n",
diff --git a/examples/notebook/examples/linear_assignment_api.ipynb b/examples/notebook/examples/linear_assignment_api.ipynb
index 67db00c482..ec8daef579 100644
--- a/examples/notebook/examples/linear_assignment_api.ipynb
+++ b/examples/notebook/examples/linear_assignment_api.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Test linear sum assignment on a 4x4 matrix.\n",
     "\n",
     "   Example taken from:\n",
@@ -95,8 +96,7 @@
     "    \"\"\"Test linear sum assignment on a 4x4 matrix.\"\"\"\n",
     "    num_sources = 4\n",
     "    num_targets = 4\n",
-    "    cost = [[90, 76, 75, 80], [35, 85, 55, 65], [125, 95, 90, 105],\n",
-    "            [45, 110, 95, 115]]\n",
+    "    cost = [[90, 76, 75, 80], [35, 85, 55, 65], [125, 95, 90, 105], [45, 110, 95, 115]]\n",
     "    expected_cost = cost[0][3] + cost[1][2] + cost[2][1] + cost[3][0]\n",
     "\n",
     "    assignment = linear_sum_assignment.SimpleLinearSumAssignment()\n",
@@ -106,21 +106,22 @@
     "\n",
     "    solve_status = assignment.solve()\n",
     "    if solve_status == assignment.OPTIMAL:\n",
-    "        print('Successful solve.')\n",
-    "        print('Total cost', assignment.optimal_cost(), '/', expected_cost)\n",
+    "        print(\"Successful solve.\")\n",
+    "        print(\"Total cost\", assignment.optimal_cost(), \"/\", expected_cost)\n",
     "        for i in range(0, assignment.num_nodes()):\n",
-    "            print('Left node %d assigned to right node %d with cost %d.' %\n",
-    "                  (i, assignment.right_mate(i), assignment.assignment_cost(i)))\n",
+    "            print(\n",
+    "                \"Left node %d assigned to right node %d with cost %d.\"\n",
+    "                % (i, assignment.right_mate(i), assignment.assignment_cost(i))\n",
+    "            )\n",
     "    elif solve_status == assignment.INFEASIBLE:\n",
-    "        print('No perfect matching exists.')\n",
+    "        print(\"No perfect matching exists.\")\n",
     "    elif solve_status == assignment.POSSIBLE_OVERFLOW:\n",
-    "        print(\n",
-    "            'Some input costs are too large and may cause an integer overflow.')\n",
+    "        print(\"Some input costs are too large and may cause an integer overflow.\")\n",
     "\n",
     "\n",
     "def main(argv: Sequence[str]) -> None:\n",
     "    if len(argv) > 1:\n",
-    "        raise app.UsageError('Too many command-line arguments.')\n",
+    "        raise app.UsageError(\"Too many command-line arguments.\")\n",
     "    run_assignment_on_4x4_matrix()\n",
     "\n",
     "\n",
diff --git a/examples/notebook/examples/linear_programming.ipynb b/examples/notebook/examples/linear_programming.ipynb
index 10c69dc593..a81c041956 100644
--- a/examples/notebook/examples/linear_programming.ipynb
+++ b/examples/notebook/examples/linear_programming.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Linear programming examples that show how to use the APIs.\n"
    ]
   },
@@ -86,8 +87,9 @@
     "\n",
     "\n",
     "def Announce(solver, api_type):\n",
-    "    print('---- Linear programming example with ' + solver + ' (' + api_type +\n",
-    "          ') -----')\n",
+    "    print(\n",
+    "        \"---- Linear programming example with \" + solver + \" (\" + api_type + \") -----\"\n",
+    "    )\n",
     "\n",
     "\n",
     "def RunLinearExampleNaturalLanguageAPI(optimization_problem_type):\n",
@@ -97,23 +99,25 @@
     "    if not solver:\n",
     "        return\n",
     "\n",
-    "    Announce(optimization_problem_type, 'natural language API')\n",
+    "    Announce(optimization_problem_type, \"natural language API\")\n",
     "\n",
     "    infinity = solver.infinity()\n",
     "    # x1, x2 and x3 are continuous non-negative variables.\n",
-    "    x1 = solver.NumVar(0.0, infinity, 'x1')\n",
-    "    x2 = solver.NumVar(0.0, infinity, 'x2')\n",
-    "    x3 = solver.NumVar(0.0, infinity, 'x3')\n",
+    "    x1 = solver.NumVar(0.0, infinity, \"x1\")\n",
+    "    x2 = solver.NumVar(0.0, infinity, \"x2\")\n",
+    "    x3 = solver.NumVar(0.0, infinity, \"x3\")\n",
     "\n",
     "    solver.Maximize(10 * x1 + 6 * x2 + 4 * x3)\n",
-    "    c0 = solver.Add(10 * x1 + 4 * x2 + 5 * x3 <= 600, 'ConstraintName0')\n",
+    "    c0 = solver.Add(10 * x1 + 4 * x2 + 5 * x3 <= 600, \"ConstraintName0\")\n",
     "    c1 = solver.Add(2 * x1 + 2 * x2 + 6 * x3 <= 300)\n",
     "    sum_of_vars = sum([x1, x2, x3])\n",
-    "    c2 = solver.Add(sum_of_vars <= 100.0, 'OtherConstraintName')\n",
+    "    c2 = solver.Add(sum_of_vars <= 100.0, \"OtherConstraintName\")\n",
     "\n",
-    "    SolveAndPrint(solver, [x1, x2, x3], [c0, c1, c2], optimization_problem_type != 'PDLP')\n",
+    "    SolveAndPrint(\n",
+    "        solver, [x1, x2, x3], [c0, c1, c2], optimization_problem_type != \"PDLP\"\n",
+    "    )\n",
     "    # Print a linear expression's solution value.\n",
-    "    print('Sum of vars: %s = %s' % (sum_of_vars, sum_of_vars.solution_value()))\n",
+    "    print(\"Sum of vars: %s = %s\" % (sum_of_vars, sum_of_vars.solution_value()))\n",
     "\n",
     "\n",
     "def RunLinearExampleCppStyleAPI(optimization_problem_type):\n",
@@ -122,13 +126,13 @@
     "    if not solver:\n",
     "        return\n",
     "\n",
-    "    Announce(optimization_problem_type, 'C++ style API')\n",
+    "    Announce(optimization_problem_type, \"C++ style API\")\n",
     "\n",
     "    infinity = solver.infinity()\n",
     "    # x1, x2 and x3 are continuous non-negative variables.\n",
-    "    x1 = solver.NumVar(0.0, infinity, 'x1')\n",
-    "    x2 = solver.NumVar(0.0, infinity, 'x2')\n",
-    "    x3 = solver.NumVar(0.0, infinity, 'x3')\n",
+    "    x1 = solver.NumVar(0.0, infinity, \"x1\")\n",
+    "    x2 = solver.NumVar(0.0, infinity, \"x2\")\n",
+    "    x3 = solver.NumVar(0.0, infinity, \"x3\")\n",
     "\n",
     "    # Maximize 10 * x1 + 6 * x2 + 4 * x3.\n",
     "    objective = solver.Objective()\n",
@@ -138,31 +142,32 @@
     "    objective.SetMaximization()\n",
     "\n",
     "    # x1 + x2 + x3 <= 100.\n",
-    "    c0 = solver.Constraint(-infinity, 100.0, 'c0')\n",
+    "    c0 = solver.Constraint(-infinity, 100.0, \"c0\")\n",
     "    c0.SetCoefficient(x1, 1)\n",
     "    c0.SetCoefficient(x2, 1)\n",
     "    c0.SetCoefficient(x3, 1)\n",
     "\n",
     "    # 10 * x1 + 4 * x2 + 5 * x3 <= 600.\n",
-    "    c1 = solver.Constraint(-infinity, 600.0, 'c1')\n",
+    "    c1 = solver.Constraint(-infinity, 600.0, \"c1\")\n",
     "    c1.SetCoefficient(x1, 10)\n",
     "    c1.SetCoefficient(x2, 4)\n",
     "    c1.SetCoefficient(x3, 5)\n",
     "\n",
     "    # 2 * x1 + 2 * x2 + 6 * x3 <= 300.\n",
-    "    c2 = solver.Constraint(-infinity, 300.0, 'c2')\n",
+    "    c2 = solver.Constraint(-infinity, 300.0, \"c2\")\n",
     "    c2.SetCoefficient(x1, 2)\n",
     "    c2.SetCoefficient(x2, 2)\n",
     "    c2.SetCoefficient(x3, 6)\n",
     "\n",
-    "    SolveAndPrint(solver, [x1, x2, x3], [c0, c1, c2],\n",
-    "                  optimization_problem_type != 'PDLP')\n",
+    "    SolveAndPrint(\n",
+    "        solver, [x1, x2, x3], [c0, c1, c2], optimization_problem_type != \"PDLP\"\n",
+    "    )\n",
     "\n",
     "\n",
     "def SolveAndPrint(solver, variable_list, constraint_list, is_precise):\n",
     "    \"\"\"Solve the problem and print the solution.\"\"\"\n",
-    "    print('Number of variables = %d' % solver.NumVariables())\n",
-    "    print('Number of constraints = %d' % solver.NumConstraints())\n",
+    "    print(\"Number of variables = %d\" % solver.NumVariables())\n",
+    "    print(\"Number of constraints = %d\" % solver.NumConstraints())\n",
     "\n",
     "    result_status = solver.Solve()\n",
     "\n",
@@ -174,37 +179,39 @@
     "    if is_precise:\n",
     "        assert solver.VerifySolution(1e-7, True)\n",
     "\n",
-    "    print('Problem solved in %f milliseconds' % solver.wall_time())\n",
+    "    print(\"Problem solved in %f milliseconds\" % solver.wall_time())\n",
     "\n",
     "    # The objective value of the solution.\n",
-    "    print('Optimal objective value = %f' % solver.Objective().Value())\n",
+    "    print(\"Optimal objective value = %f\" % solver.Objective().Value())\n",
     "\n",
     "    # The value of each variable in the solution.\n",
     "    for variable in variable_list:\n",
-    "        print('%s = %f' % (variable.name(), variable.solution_value()))\n",
+    "        print(\"%s = %f\" % (variable.name(), variable.solution_value()))\n",
     "\n",
-    "    print('Advanced usage:')\n",
-    "    print('Problem solved in %d iterations' % solver.iterations())\n",
+    "    print(\"Advanced usage:\")\n",
+    "    print(\"Problem solved in %d iterations\" % solver.iterations())\n",
     "    for variable in variable_list:\n",
-    "        print('%s: reduced cost = %f' %\n",
-    "              (variable.name(), variable.reduced_cost()))\n",
+    "        print(\"%s: reduced cost = %f\" % (variable.name(), variable.reduced_cost()))\n",
     "    activities = solver.ComputeConstraintActivities()\n",
     "    for i, constraint in enumerate(constraint_list):\n",
-    "        print(('constraint %d: dual value = %f\\n'\n",
-    "               '               activity = %f' %\n",
-    "               (i, constraint.dual_value(), activities[constraint.index()])))\n",
+    "        print(\n",
+    "            (\n",
+    "                \"constraint %d: dual value = %f\\n               activity = %f\"\n",
+    "                % (i, constraint.dual_value(), activities[constraint.index()])\n",
+    "            )\n",
+    "        )\n",
     "\n",
     "\n",
     "def main():\n",
-    "    RunLinearExampleNaturalLanguageAPI('GLOP')\n",
-    "    RunLinearExampleNaturalLanguageAPI('GLPK_LP')\n",
-    "    RunLinearExampleNaturalLanguageAPI('CLP')\n",
-    "    RunLinearExampleNaturalLanguageAPI('PDLP')\n",
+    "    RunLinearExampleNaturalLanguageAPI(\"GLOP\")\n",
+    "    RunLinearExampleNaturalLanguageAPI(\"GLPK_LP\")\n",
+    "    RunLinearExampleNaturalLanguageAPI(\"CLP\")\n",
+    "    RunLinearExampleNaturalLanguageAPI(\"PDLP\")\n",
     "\n",
-    "    RunLinearExampleCppStyleAPI('GLOP')\n",
-    "    RunLinearExampleCppStyleAPI('GLPK_LP')\n",
-    "    RunLinearExampleCppStyleAPI('CLP')\n",
-    "    RunLinearExampleCppStyleAPI('PDLP')\n",
+    "    RunLinearExampleCppStyleAPI(\"GLOP\")\n",
+    "    RunLinearExampleCppStyleAPI(\"GLPK_LP\")\n",
+    "    RunLinearExampleCppStyleAPI(\"CLP\")\n",
+    "    RunLinearExampleCppStyleAPI(\"PDLP\")\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/examples/magic_sequence_distribute.ipynb b/examples/notebook/examples/magic_sequence_distribute.ipynb
index f39a685029..838366b1af 100644
--- a/examples/notebook/examples/magic_sequence_distribute.ipynb
+++ b/examples/notebook/examples/magic_sequence_distribute.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Magic sequence problem.\n",
     "\n",
     "This models aims at building a sequence of numbers such that the number of\n",
@@ -96,12 +97,12 @@
     "\n",
     "def main(argv):\n",
     "    # Create the solver.\n",
-    "    solver = pywrapcp.Solver('magic sequence')\n",
+    "    solver = pywrapcp.Solver(\"magic sequence\")\n",
     "\n",
     "    # Create an array of IntVars to hold the answers.\n",
     "    size = int(argv[1]) if len(argv) > 1 else 100\n",
     "    all_values = list(range(0, size))\n",
-    "    all_vars = [solver.IntVar(0, size, 'vars_%d' % i) for i in all_values]\n",
+    "    all_vars = [solver.IntVar(0, size, \"vars_%d\" % i) for i in all_values]\n",
     "\n",
     "    # The number of variables equal to j shall be the value of all_vars[j].\n",
     "    solver.Add(solver.Distribute(all_vars, all_values, all_vars))\n",
@@ -111,8 +112,8 @@
     "    solver.Add(solver.Sum(all_vars) == size)\n",
     "\n",
     "    solver.NewSearch(\n",
-    "        solver.Phase(all_vars, solver.CHOOSE_FIRST_UNBOUND,\n",
-    "                     solver.ASSIGN_MIN_VALUE))\n",
+    "        solver.Phase(all_vars, solver.CHOOSE_FIRST_UNBOUND, solver.ASSIGN_MIN_VALUE)\n",
+    "    )\n",
     "    solver.NextSolution()\n",
     "    print(all_vars)\n",
     "    solver.EndSearch()\n",
diff --git a/examples/notebook/examples/maze_escape_sat.ipynb b/examples/notebook/examples/maze_escape_sat.ipynb
index 4e50020510..ce11029b4c 100644
--- a/examples/notebook/examples/maze_escape_sat.ipynb
+++ b/examples/notebook/examples/maze_escape_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Excape the maze while collecting treasures in order.\n",
     "\n",
     "The path must begin at the 'start' position, finish at the 'end' position,\n",
@@ -94,23 +95,29 @@
     "from ortools.sat.python import cp_model\n",
     "\n",
     "_OUTPUT_PROTO = flags.DEFINE_string(\n",
-    "    'output_proto', '', 'Output file to write the cp_model proto to.')\n",
-    "_PARAMS = flags.DEFINE_string('params',\n",
-    "                              'num_search_workers:8,log_search_progress:true',\n",
-    "                              'Sat solver parameters.')\n",
+    "    \"output_proto\", \"\", \"Output file to write the cp_model proto to.\"\n",
+    ")\n",
+    "_PARAMS = flags.DEFINE_string(\n",
+    "    \"params\", \"num_search_workers:8,log_search_progress:true\", \"Sat solver parameters.\"\n",
+    ")\n",
     "\n",
     "\n",
-    "def add_neighbor(size, x, y, z, dx, dy, dz, model, index_map, position_to_rank,\n",
-    "                 arcs):\n",
+    "def add_neighbor(size, x, y, z, dx, dy, dz, model, index_map, position_to_rank, arcs):\n",
     "    \"\"\"Checks if the neighbor is valid, and adds it to the model.\"\"\"\n",
-    "    if (x + dx < 0 or x + dx >= size or y + dy < 0 or y + dy >= size or\n",
-    "            z + dz < 0 or z + dz >= size):\n",
+    "    if (\n",
+    "        x + dx < 0\n",
+    "        or x + dx >= size\n",
+    "        or y + dy < 0\n",
+    "        or y + dy >= size\n",
+    "        or z + dz < 0\n",
+    "        or z + dz >= size\n",
+    "    ):\n",
     "        return\n",
     "    before_index = index_map[(x, y, z)]\n",
     "    before_rank = position_to_rank[(x, y, z)]\n",
     "    after_index = index_map[(x + dx, y + dy, z + dz)]\n",
     "    after_rank = position_to_rank[(x + dx, y + dy, z + dz)]\n",
-    "    move_literal = model.NewBoolVar('')\n",
+    "    move_literal = model.NewBoolVar(\"\")\n",
     "    model.Add(after_rank == before_rank + 1).OnlyEnforceIf(move_literal)\n",
     "    arcs.append((before_index, after_index, move_literal))\n",
     "\n",
@@ -139,8 +146,7 @@
     "    position_to_rank = {}\n",
     "\n",
     "    for coord in reverse_map:\n",
-    "        position_to_rank[coord] = model.NewIntVar(0, counter - 1,\n",
-    "                                                  f'rank_{coord}')\n",
+    "        position_to_rank[coord] = model.NewIntVar(0, counter - 1, f\"rank_{coord}\")\n",
     "\n",
     "    # Path constraints.\n",
     "    model.Add(position_to_rank[start] == 0)\n",
@@ -154,18 +160,24 @@
     "    for x in range(size):\n",
     "        for y in range(size):\n",
     "            for z in range(size):\n",
-    "                add_neighbor(size, x, y, z, -1, 0, 0, model, index_map,\n",
-    "                             position_to_rank, arcs)\n",
-    "                add_neighbor(size, x, y, z, 1, 0, 0, model, index_map,\n",
-    "                             position_to_rank, arcs)\n",
-    "                add_neighbor(size, x, y, z, 0, -1, 0, model, index_map,\n",
-    "                             position_to_rank, arcs)\n",
-    "                add_neighbor(size, x, y, z, 0, 1, 0, model, index_map,\n",
-    "                             position_to_rank, arcs)\n",
-    "                add_neighbor(size, x, y, z, 0, 0, -1, model, index_map,\n",
-    "                             position_to_rank, arcs)\n",
-    "                add_neighbor(size, x, y, z, 0, 0, 1, model, index_map,\n",
-    "                             position_to_rank, arcs)\n",
+    "                add_neighbor(\n",
+    "                    size, x, y, z, -1, 0, 0, model, index_map, position_to_rank, arcs\n",
+    "                )\n",
+    "                add_neighbor(\n",
+    "                    size, x, y, z, 1, 0, 0, model, index_map, position_to_rank, arcs\n",
+    "                )\n",
+    "                add_neighbor(\n",
+    "                    size, x, y, z, 0, -1, 0, model, index_map, position_to_rank, arcs\n",
+    "                )\n",
+    "                add_neighbor(\n",
+    "                    size, x, y, z, 0, 1, 0, model, index_map, position_to_rank, arcs\n",
+    "                )\n",
+    "                add_neighbor(\n",
+    "                    size, x, y, z, 0, 0, -1, model, index_map, position_to_rank, arcs\n",
+    "                )\n",
+    "                add_neighbor(\n",
+    "                    size, x, y, z, 0, 0, 1, model, index_map, position_to_rank, arcs\n",
+    "                )\n",
     "\n",
     "    # Closes the loop as the constraint expects a circuit, not a path.\n",
     "    arcs.append((index_map[end], index_map[start], True))\n",
@@ -186,28 +198,28 @@
     "\n",
     "    # Prints solution.\n",
     "    if result == cp_model.OPTIMAL:\n",
-    "        path = [''] * counter\n",
+    "        path = [\"\"] * counter\n",
     "        for x in range(size):\n",
     "            for y in range(size):\n",
     "                for z in range(size):\n",
     "                    position = (x, y, z)\n",
     "                    rank = solver.Value(position_to_rank[position])\n",
-    "                    msg = f'({x}, {y}, {z})'\n",
+    "                    msg = f\"({x}, {y}, {z})\"\n",
     "                    if position == start:\n",
-    "                        msg += ' [start]'\n",
+    "                        msg += \" [start]\"\n",
     "                    elif position == end:\n",
-    "                        msg += ' [end]'\n",
+    "                        msg += \" [end]\"\n",
     "                    else:\n",
     "                        for b in range(len(boxes)):\n",
     "                            if position == boxes[b]:\n",
-    "                                msg += f' [boxes {b}]'\n",
+    "                                msg += f\" [boxes {b}]\"\n",
     "                    path[rank] = msg\n",
     "        print(path)\n",
     "\n",
     "\n",
     "def main(argv: Sequence[str]) -> None:\n",
     "    if len(argv) > 1:\n",
-    "        raise app.UsageError('Too many command-line arguments.')\n",
+    "        raise app.UsageError(\"Too many command-line arguments.\")\n",
     "    escape_the_maze(_PARAMS.value, _OUTPUT_PROTO.value)\n",
     "\n",
     "\n",
diff --git a/examples/notebook/examples/no_wait_baking_scheduling_sat.ipynb b/examples/notebook/examples/no_wait_baking_scheduling_sat.ipynb
index ba553a817c..890a49ce74 100644
--- a/examples/notebook/examples/no_wait_baking_scheduling_sat.ipynb
+++ b/examples/notebook/examples/no_wait_baking_scheduling_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Scheduling cooking tasks in a bakery using no-wait jobshop scheduling.\n",
     "\n",
     "We are scheduling a full day of baking:\n",
@@ -93,34 +94,35 @@
     "from google.protobuf import text_format\n",
     "from ortools.sat.python import cp_model\n",
     "\n",
-    "_PARAMS = flags.DEFINE_string('params',\n",
-    "                              'num_search_workers:16, max_time_in_seconds:30',\n",
-    "                              'Sat solver parameters.')\n",
+    "_PARAMS = flags.DEFINE_string(\n",
+    "    \"params\", \"num_search_workers:16, max_time_in_seconds:30\", \"Sat solver parameters.\"\n",
+    ")\n",
     "_PROTO_FILE = flags.DEFINE_string(\n",
-    "    'proto_file', '', 'If not empty, output the proto to this file.')\n",
+    "    \"proto_file\", \"\", \"If not empty, output the proto to this file.\"\n",
+    ")\n",
     "\n",
     "# Recipes\n",
-    "CROISSANT = 'croissant'\n",
-    "APPLE_PIE = 'apple pie'\n",
-    "BRIOCHE = 'brioche'\n",
-    "CHOCOLATE_CAKE = 'chocolate cake'\n",
+    "CROISSANT = \"croissant\"\n",
+    "APPLE_PIE = \"apple pie\"\n",
+    "BRIOCHE = \"brioche\"\n",
+    "CHOCOLATE_CAKE = \"chocolate cake\"\n",
     "\n",
     "# Skills\n",
-    "BAKING = 'baking'\n",
-    "PROOFING = 'proofing'\n",
-    "COOKING = 'cooking'\n",
-    "COOLING = 'cooling'\n",
-    "DECORATING = 'decorating'\n",
-    "DISPLAY = 'display'\n",
+    "BAKING = \"baking\"\n",
+    "PROOFING = \"proofing\"\n",
+    "COOKING = \"cooking\"\n",
+    "COOLING = \"cooling\"\n",
+    "DECORATING = \"decorating\"\n",
+    "DISPLAY = \"display\"\n",
     "\n",
     "\n",
     "class Task(object):\n",
     "    \"\"\"A unit baking task.\n",
     "\n",
-    "  - Simple baking tasks have a fixed duration. They are performed by workers.\n",
-    "  - Waiting/cooling/proofing tasks have a min and a max duration.\n",
-    "    They are performed by machine or they use space resources.\n",
-    "  \"\"\"\n",
+    "    - Simple baking tasks have a fixed duration. They are performed by workers.\n",
+    "    - Waiting/cooling/proofing tasks have a min and a max duration.\n",
+    "      They are performed by machine or they use space resources.\n",
+    "    \"\"\"\n",
     "\n",
     "    def __init__(self, name, min_duration, max_duration):\n",
     "        self.name = name\n",
@@ -152,13 +154,13 @@
     "class Resource(object):\n",
     "    \"\"\"A resource is a worker, a machine, or just some space for cakes to rest.\n",
     "\n",
-    "  - Workers have a capacity of 1 and can have variable efficiency.\n",
-    "  - Machines and spaces have a capacity greater or equal to one, but the\n",
-    "    efficiency is fixed to 100.\n",
+    "    - Workers have a capacity of 1 and can have variable efficiency.\n",
+    "    - Machines and spaces have a capacity greater or equal to one, but the\n",
+    "      efficiency is fixed to 100.\n",
     "\n",
-    "    For a worker with efficiency k and a task of duration t, the resulting\n",
-    "    work will have a duration `ceil(t * k)`.\n",
-    "  \"\"\"\n",
+    "      For a worker with efficiency k and a task of duration t, the resulting\n",
+    "      work will have a duration `ceil(t * k)`.\n",
+    "    \"\"\"\n",
     "\n",
     "    def __init__(self, name, capacity):\n",
     "        self.name = name\n",
@@ -176,12 +178,12 @@
     "    def __init__(self, unique_id, recipe_name, due_date, quantity):\n",
     "        \"\"\"Builds an order.\n",
     "\n",
-    "    Args:\n",
-    "      unique_id: A unique identifier for the order. Used to display the result.\n",
-    "      recipe_name: The name of the recipe. It must match one of the recipes.\n",
-    "      due_date: The due date in minutes since midnight.\n",
-    "      quantity: How many cakes to prepare.\n",
-    "    \"\"\"\n",
+    "        Args:\n",
+    "          unique_id: A unique identifier for the order. Used to display the result.\n",
+    "          recipe_name: The name of the recipe. It must match one of the recipes.\n",
+    "          due_date: The due date in minutes since midnight.\n",
+    "          quantity: How many cakes to prepare.\n",
+    "        \"\"\"\n",
     "        self.unique_id = unique_id\n",
     "        self.recipe_name = recipe_name\n",
     "        self.due_date = due_date\n",
@@ -217,32 +219,41 @@
     "    chocolate_cake_recipe.add_task(DECORATING, 15, 15)\n",
     "    chocolate_cake_recipe.add_task(DISPLAY, 5, 5 * 60)\n",
     "    recipes = [\n",
-    "        croissant_recipe, apple_pie_recipe, brioche_recipe,\n",
-    "        chocolate_cake_recipe\n",
+    "        croissant_recipe,\n",
+    "        apple_pie_recipe,\n",
+    "        brioche_recipe,\n",
+    "        chocolate_cake_recipe,\n",
     "    ]\n",
     "\n",
     "    # Resources.\n",
-    "    baker1 = Resource('baker1', 1).add_skill(BAKING, 1.0)\n",
-    "    baker2 = Resource('baker2', 1).add_skill(BAKING, 1.0)\n",
-    "    decorator1 = Resource('decorator1', 1).add_skill(DECORATING, 1.0)\n",
-    "    waiting_space = Resource('waiting_space', 4).add_skill(PROOFING, 1.0)\n",
-    "    oven = Resource('oven', 4).add_skill(COOKING, 1.0)\n",
-    "    display_space = Resource('display_space', 12).add_skill(DISPLAY, 1.0)\n",
+    "    baker1 = Resource(\"baker1\", 1).add_skill(BAKING, 1.0)\n",
+    "    baker2 = Resource(\"baker2\", 1).add_skill(BAKING, 1.0)\n",
+    "    decorator1 = Resource(\"decorator1\", 1).add_skill(DECORATING, 1.0)\n",
+    "    waiting_space = Resource(\"waiting_space\", 4).add_skill(PROOFING, 1.0)\n",
+    "    oven = Resource(\"oven\", 4).add_skill(COOKING, 1.0)\n",
+    "    display_space = Resource(\"display_space\", 12).add_skill(DISPLAY, 1.0)\n",
     "    resources = [baker1, baker2, decorator1, waiting_space, oven, display_space]\n",
     "\n",
     "    # Orders\n",
-    "    croissant_7am = Order('croissant_7am', CROISSANT, 7 * 60, 3)\n",
-    "    croissant_8am = Order('croissant_8am', CROISSANT, 8 * 60, 3)\n",
-    "    croissant_9am = Order('croissant_9am', CROISSANT, 9 * 60, 2)\n",
-    "    croissant_10am = Order('croissant_10am', CROISSANT, 10 * 60, 1)\n",
-    "    croissant_11am = Order('croissant_11am', CROISSANT, 11 * 60, 1)\n",
-    "    brioche_10am = Order('brioche_10am', BRIOCHE, 10 * 60, 8)\n",
-    "    brioche_12pm = Order('brioche_12pm', BRIOCHE, 12 * 60, 8)\n",
-    "    apple_pie_1pm = Order('apple_pie_1pm', APPLE_PIE, 13 * 60, 10)\n",
-    "    chocolate_4pm = Order('chocolate_4pm', CHOCOLATE_CAKE, 16 * 60, 10)\n",
+    "    croissant_7am = Order(\"croissant_7am\", CROISSANT, 7 * 60, 3)\n",
+    "    croissant_8am = Order(\"croissant_8am\", CROISSANT, 8 * 60, 3)\n",
+    "    croissant_9am = Order(\"croissant_9am\", CROISSANT, 9 * 60, 2)\n",
+    "    croissant_10am = Order(\"croissant_10am\", CROISSANT, 10 * 60, 1)\n",
+    "    croissant_11am = Order(\"croissant_11am\", CROISSANT, 11 * 60, 1)\n",
+    "    brioche_10am = Order(\"brioche_10am\", BRIOCHE, 10 * 60, 8)\n",
+    "    brioche_12pm = Order(\"brioche_12pm\", BRIOCHE, 12 * 60, 8)\n",
+    "    apple_pie_1pm = Order(\"apple_pie_1pm\", APPLE_PIE, 13 * 60, 10)\n",
+    "    chocolate_4pm = Order(\"chocolate_4pm\", CHOCOLATE_CAKE, 16 * 60, 10)\n",
     "    orders = [\n",
-    "        croissant_7am, croissant_8am, croissant_9am, croissant_10am,\n",
-    "        croissant_11am, brioche_10am, brioche_12pm, apple_pie_1pm, chocolate_4pm\n",
+    "        croissant_7am,\n",
+    "        croissant_8am,\n",
+    "        croissant_9am,\n",
+    "        croissant_10am,\n",
+    "        croissant_11am,\n",
+    "        brioche_10am,\n",
+    "        brioche_12pm,\n",
+    "        apple_pie_1pm,\n",
+    "        chocolate_4pm,\n",
     "    ]\n",
     "\n",
     "    return recipes, resources, orders\n",
@@ -276,50 +287,48 @@
     "    tardiness_vars = []\n",
     "    for order in orders:\n",
     "        for batch in range(order.quantity):\n",
-    "            order_id = f'{order.unique_id}_{batch}'\n",
+    "            order_id = f\"{order.unique_id}_{batch}\"\n",
     "            sorted_orders.append(order_id)\n",
     "            previous_end = None\n",
     "            due_date = order.due_date\n",
     "            recipe = recipe_by_name[order.recipe_name]\n",
     "            for task in recipe.tasks:\n",
     "                skill_name = task.name\n",
-    "                suffix = f'_{order.unique_id}_batch{batch}_{skill_name}'\n",
+    "                suffix = f\"_{order.unique_id}_batch{batch}_{skill_name}\"\n",
     "\n",
     "                start = None\n",
     "                if previous_end is None:\n",
-    "                    start = model.NewIntVar(start_work, horizon,\n",
-    "                                            f'start{suffix}')\n",
+    "                    start = model.NewIntVar(start_work, horizon, f\"start{suffix}\")\n",
     "                    orders_sequence_of_events[order_id].append(\n",
-    "                        (start, f'start{suffix}'))\n",
+    "                        (start, f\"start{suffix}\")\n",
+    "                    )\n",
     "                else:\n",
     "                    start = previous_end\n",
     "\n",
-    "                size = model.NewIntVar(task.min_duration, task.max_duration,\n",
-    "                                       f'size{suffix}')\n",
+    "                size = model.NewIntVar(\n",
+    "                    task.min_duration, task.max_duration, f\"size{suffix}\"\n",
+    "                )\n",
     "                end = None\n",
     "                if task == recipe.tasks[-1]:\n",
     "                    # The order must end after the due_date. Ideally, exactly at the\n",
     "                    # due_date.\n",
-    "                    tardiness = model.NewIntVar(0, horizon - due_date,\n",
-    "                                                f'end{suffix}')\n",
+    "                    tardiness = model.NewIntVar(0, horizon - due_date, f\"end{suffix}\")\n",
     "                    end = tardiness + due_date\n",
     "\n",
     "                    # Store the end_var for the objective.\n",
     "                    tardiness_vars.append(tardiness)\n",
     "                else:\n",
-    "                    end = model.NewIntVar(start_work, horizon, f'end{suffix}')\n",
-    "                orders_sequence_of_events[order_id].append(\n",
-    "                    (end, f'end{suffix}'))\n",
+    "                    end = model.NewIntVar(start_work, horizon, f\"end{suffix}\")\n",
+    "                orders_sequence_of_events[order_id].append((end, f\"end{suffix}\"))\n",
     "                previous_end = end\n",
     "\n",
     "                # Per resource copy.\n",
     "                presence_literals = []\n",
     "                for resource in resource_list_by_skill_name[skill_name]:\n",
-    "                    presence = model.NewBoolVar(\n",
-    "                        f'presence{suffix}_{resource.name}')\n",
+    "                    presence = model.NewBoolVar(f\"presence{suffix}_{resource.name}\")\n",
     "                    copy = model.NewOptionalIntervalVar(\n",
-    "                        start, size, end, presence,\n",
-    "                        f'interval{suffix}_{resource.name}')\n",
+    "                        start, size, end, presence, f\"interval{suffix}_{resource.name}\"\n",
+    "                    )\n",
     "                    interval_list_by_resource_name[resource.name].append(copy)\n",
     "                    presence_literals.append(presence)\n",
     "\n",
@@ -332,8 +341,7 @@
     "        if resource.capacity == 1:\n",
     "            model.AddNoOverlap(intervals)\n",
     "        else:\n",
-    "            model.AddCumulative(intervals, [1] * len(intervals),\n",
-    "                                resource.capacity)\n",
+    "            model.AddCumulative(intervals, [1] * len(intervals), resource.capacity)\n",
     "\n",
     "    # The objective is to minimize the sum of the tardiness values of each jobs.\n",
     "    # The tardiness is difference between the end time of an order and its\n",
@@ -349,15 +357,15 @@
     "\n",
     "    if status == cp_model.OPTIMAL or status == cp_model.FEASIBLE:\n",
     "        for order_id in sorted_orders:\n",
-    "            print(f'{order_id}:')\n",
+    "            print(f\"{order_id}:\")\n",
     "            for time_expr, event_id in orders_sequence_of_events[order_id]:\n",
     "                time = solver.Value(time_expr)\n",
-    "                print(f'  {event_id} at {time // 60}:{time % 60:02}')\n",
+    "                print(f\"  {event_id} at {time // 60}:{time % 60:02}\")\n",
     "\n",
     "\n",
     "def main(argv: Sequence[str]) -> None:\n",
     "    if len(argv) > 1:\n",
-    "        raise app.UsageError('Too many command-line arguments.')\n",
+    "        raise app.UsageError(\"Too many command-line arguments.\")\n",
     "\n",
     "    recipes, resources, orders = set_up_data()\n",
     "    solve_with_cp_sat(recipes, resources, orders)\n",
diff --git a/examples/notebook/examples/nqueens_sat.ipynb b/examples/notebook/examples/nqueens_sat.ipynb
index 0a115099b4..7eb7710a51 100644
--- a/examples/notebook/examples/nqueens_sat.ipynb
+++ b/examples/notebook/examples/nqueens_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "CP/SAT model for the N-queens problem.\n"
    ]
   },
@@ -86,7 +87,7 @@
     "\n",
     "from ortools.sat.python import cp_model\n",
     "\n",
-    "_SIZE = flags.DEFINE_integer('size', 8, 'Number of queens.')\n",
+    "_SIZE = flags.DEFINE_integer(\"size\", 8, \"Number of queens.\")\n",
     "\n",
     "\n",
     "class NQueenSolutionPrinter(cp_model.CpSolverSolutionCallback):\n",
@@ -103,8 +104,10 @@
     "\n",
     "    def on_solution_callback(self):\n",
     "        current_time = time.time()\n",
-    "        print('Solution %i, time = %f s' %\n",
-    "              (self.__solution_count, current_time - self.__start_time))\n",
+    "        print(\n",
+    "            \"Solution %i, time = %f s\"\n",
+    "            % (self.__solution_count, current_time - self.__start_time)\n",
+    "        )\n",
     "        self.__solution_count += 1\n",
     "\n",
     "        all_queens = range(len(self.__queens))\n",
@@ -112,9 +115,9 @@
     "            for j in all_queens:\n",
     "                if self.Value(self.__queens[j]) == i:\n",
     "                    # There is a queen in column j, row i.\n",
-    "                    print('Q', end=' ')\n",
+    "                    print(\"Q\", end=\" \")\n",
     "                else:\n",
-    "                    print('_', end=' ')\n",
+    "                    print(\"_\", end=\" \")\n",
     "            print()\n",
     "        print()\n",
     "\n",
@@ -127,9 +130,7 @@
     "\n",
     "    ### Creates the variables.\n",
     "    # The array index is the column, and the value is the row.\n",
-    "    queens = [\n",
-    "        model.NewIntVar(0, board_size - 1, 'x%i' % i) for i in range(board_size)\n",
-    "    ]\n",
+    "    queens = [model.NewIntVar(0, board_size - 1, \"x%i\" % i) for i in range(board_size)]\n",
     "\n",
     "    ### Creates the constraints.\n",
     "\n",
@@ -141,8 +142,8 @@
     "    diag1 = []\n",
     "    diag2 = []\n",
     "    for i in range(board_size):\n",
-    "        q1 = model.NewIntVar(0, 2 * board_size, 'diag1_%i' % i)\n",
-    "        q2 = model.NewIntVar(-board_size, board_size, 'diag2_%i' % i)\n",
+    "        q1 = model.NewIntVar(0, 2 * board_size, \"diag1_%i\" % i)\n",
+    "        q2 = model.NewIntVar(-board_size, board_size, \"diag2_%i\" % i)\n",
     "        diag1.append(q1)\n",
     "        diag2.append(q2)\n",
     "        model.Add(q1 == queens[i] + i)\n",
@@ -159,11 +160,11 @@
     "    solver.Solve(model, solution_printer)\n",
     "\n",
     "    print()\n",
-    "    print('Statistics')\n",
-    "    print('  - conflicts       : %i' % solver.NumConflicts())\n",
-    "    print('  - branches        : %i' % solver.NumBranches())\n",
-    "    print('  - wall time       : %f s' % solver.WallTime())\n",
-    "    print('  - solutions found : %i' % solution_printer.SolutionCount())\n",
+    "    print(\"Statistics\")\n",
+    "    print(\"  - conflicts       : %i\" % solver.NumConflicts())\n",
+    "    print(\"  - branches        : %i\" % solver.NumBranches())\n",
+    "    print(\"  - wall time       : %f s\" % solver.WallTime())\n",
+    "    print(\"  - solutions found : %i\" % solution_printer.SolutionCount())\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/examples/prize_collecting_tsp_sat.ipynb b/examples/notebook/examples/prize_collecting_tsp_sat.ipynb
index 8df28cecf3..fb90b4c8ec 100644
--- a/examples/notebook/examples/prize_collecting_tsp_sat.ipynb
+++ b/examples/notebook/examples/prize_collecting_tsp_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Simple prize collecting TSP problem with a max distance.\n"
    ]
   },
@@ -87,6 +88,7 @@
     "\n",
     "\n",
     "DISTANCE_MATRIX = [\n",
+    "    # fmt:off\n",
     "    [0, 10938, 4542, 2835, 29441, 2171, 1611, 9208, 9528, 11111, 16120, 22606, 22127, 20627, 21246, 23387, 16697, 33609, 26184, 24772, 22644, 20655, 30492, 23296, 32979, 18141, 19248, 17129, 17192, 15645, 12658, 11210, 12094, 13175, 18162, 4968, 12308, 10084, 13026, 15056],\n",
     "    [10938, 0, 6422, 9742, 18988, 12974, 11216, 19715, 19004, 18271, 25070, 31971, 31632, 30571, 31578, 33841, 27315, 43964, 36944, 35689, 33569, 31481, 41360, 33760, 43631, 28730, 29976, 27803, 28076, 26408, 23504, 22025, 22000, 13197, 14936, 15146, 23246, 20956, 23963, 25994],\n",
     "    [4542, 6422, 0, 3644, 25173, 6552, 5092, 13584, 13372, 13766, 19805, 26537, 26117, 24804, 25590, 27784, 21148, 37981, 30693, 29315, 27148, 25071, 34943, 27472, 37281, 22389, 23592, 21433, 21655, 20011, 17087, 15612, 15872, 11653, 15666, 8842, 16843, 14618, 17563, 19589],\n",
@@ -127,7 +129,8 @@
     "    [10084, 20956, 14618, 12135, 38935, 8306, 9793, 2615, 5850, 10467, 9918, 14568, 13907, 11803, 11750, 13657, 6901, 23862, 16125, 14748, 12981, 11624, 21033, 15358, 24144, 10304, 10742, 9094, 8042, 7408, 4580, 4072, 8446, 20543, 26181, 7668, 2747, 0, 3330, 5313],\n",
     "    [13026, 23963, 17563, 14771, 42160, 11069, 12925, 5730, 8778, 13375, 11235, 14366, 13621, 11188, 10424, 11907, 5609, 21861, 13624, 11781, 9718, 8304, 17737, 12200, 20816, 7330, 7532, 6117, 4735, 4488, 2599, 3355, 7773, 22186, 27895, 9742, 726, 3330, 0, 2042],\n",
     "    [15056, 25994, 19589, 16743, 44198, 13078, 14967, 7552, 10422, 14935, 11891, 14002, 13225, 10671, 9475, 10633, 5084, 20315, 11866, 9802, 7682, 6471, 15720, 10674, 18908, 6204, 6000, 5066, 3039, 3721, 3496, 4772, 8614, 23805, 29519, 11614, 2749, 5313, 2042, 0],\n",
-    "]  # yapf: disable\n",
+    "    # fmt:on\n",
+    "]\n",
     "\n",
     "MAX_DISTANCE = 80_000\n",
     "\n",
@@ -139,22 +142,22 @@
     "def print_solution(solver, visited_nodes, used_arcs, num_nodes):\n",
     "    \"\"\"Prints solution on console.\"\"\"\n",
     "    # Display dropped nodes.\n",
-    "    dropped_nodes = 'Dropped nodes:'\n",
+    "    dropped_nodes = \"Dropped nodes:\"\n",
     "    for i in range(num_nodes):\n",
     "        if i == 0:\n",
     "            continue\n",
     "        if not solver.BooleanValue(visited_nodes[i]):\n",
-    "            dropped_nodes += f' {i}({VISIT_VALUES[i]})'\n",
+    "            dropped_nodes += f\" {i}({VISIT_VALUES[i]})\"\n",
     "    print(dropped_nodes)\n",
     "    # Display routes\n",
     "    current_node = 0\n",
-    "    plan_output = 'Route for vehicle 0:\\n'\n",
+    "    plan_output = \"Route for vehicle 0:\\n\"\n",
     "    route_distance = 0\n",
     "    value_collected = 0\n",
     "    route_is_finished = False\n",
     "    while not route_is_finished:\n",
     "        value_collected += VISIT_VALUES[current_node]\n",
-    "        plan_output += f' {current_node} ->'\n",
+    "        plan_output += f\" {current_node} ->\"\n",
     "        # find next node\n",
     "        for node in range(num_nodes):\n",
     "            if node == current_node:\n",
@@ -165,9 +168,9 @@
     "                if current_node == 0:\n",
     "                    route_is_finished = True\n",
     "                break\n",
-    "    plan_output += f' {current_node}\\n'\n",
-    "    plan_output += f'Distance of the route: {route_distance}m\\n'\n",
-    "    plan_output += f'Value collected: {value_collected}/{sum(VISIT_VALUES)}\\n'\n",
+    "    plan_output += f\" {current_node}\\n\"\n",
+    "    plan_output += f\"Distance of the route: {route_distance}m\\n\"\n",
+    "    plan_output += f\"Value collected: {value_collected}/{sum(VISIT_VALUES)}\\n\"\n",
     "    print(plan_output)\n",
     "\n",
     "\n",
@@ -175,7 +178,7 @@
     "    \"\"\"Entry point of the program.\"\"\"\n",
     "    num_nodes = len(DISTANCE_MATRIX)\n",
     "    all_nodes = range(num_nodes)\n",
-    "    print(f'Num nodes = {num_nodes}')\n",
+    "    print(f\"Num nodes = {num_nodes}\")\n",
     "\n",
     "    # Model.\n",
     "    model = cp_model.CpModel()\n",
@@ -188,8 +191,8 @@
     "    # Create the circuit constraint.\n",
     "    arcs = []\n",
     "    for i in all_nodes:\n",
-    "        is_visited = model.NewBoolVar(f'{i} is visited')\n",
-    "        arcs.append([i, i, is_visited.Not()])\n",
+    "        is_visited = model.NewBoolVar(f\"{i} is visited\")\n",
+    "        arcs.append((i, i, is_visited.Not()))\n",
     "\n",
     "        obj_vars.append(is_visited)\n",
     "        obj_coeffs.append(VISIT_VALUES[i])\n",
@@ -199,8 +202,8 @@
     "            if i == j:\n",
     "                used_arcs[i, j] = is_visited.Not()\n",
     "                continue\n",
-    "            arc_is_used = model.NewBoolVar(f'{j} follows {i}')\n",
-    "            arcs.append([i, j, arc_is_used])\n",
+    "            arc_is_used = model.NewBoolVar(f\"{j} follows {i}\")\n",
+    "            arcs.append((i, j, arc_is_used))\n",
     "\n",
     "            obj_vars.append(arc_is_used)\n",
     "            obj_coeffs.append(-DISTANCE_MATRIX[i][j])\n",
@@ -213,13 +216,16 @@
     "\n",
     "    # limit the route distance\n",
     "    model.Add(\n",
-    "        sum(used_arcs[i, j] * DISTANCE_MATRIX[i][j]\n",
+    "        sum(\n",
+    "            used_arcs[i, j] * DISTANCE_MATRIX[i][j]\n",
     "            for i in all_nodes\n",
-    "            for j in all_nodes) <= MAX_DISTANCE)\n",
+    "            for j in all_nodes\n",
+    "        )\n",
+    "        <= MAX_DISTANCE\n",
+    "    )\n",
     "\n",
     "    # Maximize visited node values minus the travelled distance.\n",
-    "    model.Maximize(\n",
-    "        sum(obj_vars[i] * obj_coeffs[i] for i in range(len(obj_vars))))\n",
+    "    model.Maximize(sum(obj_vars[i] * obj_coeffs[i] for i in range(len(obj_vars))))\n",
     "\n",
     "    # Solve and print out the solution.\n",
     "    solver = cp_model.CpSolver()\n",
@@ -235,7 +241,7 @@
     "\n",
     "def main(argv: Sequence[str]) -> None:\n",
     "    if len(argv) > 1:\n",
-    "        raise app.UsageError('Too many command-line arguments.')\n",
+    "        raise app.UsageError(\"Too many command-line arguments.\")\n",
     "    prize_collecting_tsp()\n",
     "\n",
     "\n",
diff --git a/examples/notebook/examples/prize_collecting_vrp_sat.ipynb b/examples/notebook/examples/prize_collecting_vrp_sat.ipynb
index 8dfb6d7e18..47a8ac78df 100644
--- a/examples/notebook/examples/prize_collecting_vrp_sat.ipynb
+++ b/examples/notebook/examples/prize_collecting_vrp_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Simple prize collecting VRP problem with a max distance.\n"
    ]
   },
@@ -87,6 +88,7 @@
     "\n",
     "\n",
     "DISTANCE_MATRIX = [\n",
+    "    # fmt:off\n",
     "    [0, 10938, 4542, 2835, 29441, 2171, 1611, 9208, 9528, 11111, 16120, 22606, 22127, 20627, 21246, 23387, 16697, 33609, 26184, 24772, 22644, 20655, 30492, 23296, 32979, 18141, 19248, 17129, 17192, 15645, 12658, 11210, 12094, 13175, 18162, 4968, 12308, 10084, 13026, 15056],\n",
     "    [10938, 0, 6422, 9742, 18988, 12974, 11216, 19715, 19004, 18271, 25070, 31971, 31632, 30571, 31578, 33841, 27315, 43964, 36944, 35689, 33569, 31481, 41360, 33760, 43631, 28730, 29976, 27803, 28076, 26408, 23504, 22025, 22000, 13197, 14936, 15146, 23246, 20956, 23963, 25994],\n",
     "    [4542, 6422, 0, 3644, 25173, 6552, 5092, 13584, 13372, 13766, 19805, 26537, 26117, 24804, 25590, 27784, 21148, 37981, 30693, 29315, 27148, 25071, 34943, 27472, 37281, 22389, 23592, 21433, 21655, 20011, 17087, 15612, 15872, 11653, 15666, 8842, 16843, 14618, 17563, 19589],\n",
@@ -127,7 +129,8 @@
     "    [10084, 20956, 14618, 12135, 38935, 8306, 9793, 2615, 5850, 10467, 9918, 14568, 13907, 11803, 11750, 13657, 6901, 23862, 16125, 14748, 12981, 11624, 21033, 15358, 24144, 10304, 10742, 9094, 8042, 7408, 4580, 4072, 8446, 20543, 26181, 7668, 2747, 0, 3330, 5313],\n",
     "    [13026, 23963, 17563, 14771, 42160, 11069, 12925, 5730, 8778, 13375, 11235, 14366, 13621, 11188, 10424, 11907, 5609, 21861, 13624, 11781, 9718, 8304, 17737, 12200, 20816, 7330, 7532, 6117, 4735, 4488, 2599, 3355, 7773, 22186, 27895, 9742, 726, 3330, 0, 2042],\n",
     "    [15056, 25994, 19589, 16743, 44198, 13078, 14967, 7552, 10422, 14935, 11891, 14002, 13225, 10671, 9475, 10633, 5084, 20315, 11866, 9802, 7682, 6471, 15720, 10674, 18908, 6204, 6000, 5066, 3039, 3721, 3496, 4772, 8614, 23805, 29519, 11614, 2749, 5313, 2042, 0],\n",
-    "]  # yapf: disable\n",
+    "    # fmt:on\n",
+    "]\n",
     "\n",
     "MAX_DISTANCE = 80_000\n",
     "\n",
@@ -139,29 +142,28 @@
     "def print_solution(solver, visited_nodes, used_arcs, num_nodes, num_vehicles):\n",
     "    \"\"\"Prints solution on console.\"\"\"\n",
     "    # Display dropped nodes.\n",
-    "    dropped_nodes = 'Dropped nodes:'\n",
+    "    dropped_nodes = \"Dropped nodes:\"\n",
     "    for node in range(num_nodes):\n",
     "        if node == 0:\n",
     "            continue\n",
-    "        is_visited = sum([\n",
-    "            solver.BooleanValue(visited_nodes[v][node])\n",
-    "            for v in range(num_vehicles)\n",
-    "        ])\n",
+    "        is_visited = sum(\n",
+    "            [solver.BooleanValue(visited_nodes[v][node]) for v in range(num_vehicles)]\n",
+    "        )\n",
     "        if not is_visited:\n",
-    "            dropped_nodes += f' {node}({VISIT_VALUES[node]})'\n",
+    "            dropped_nodes += f\" {node}({VISIT_VALUES[node]})\"\n",
     "    print(dropped_nodes)\n",
     "    # Display routes\n",
     "    total_distance = 0\n",
     "    total_value_collected = 0\n",
     "    for v in range(num_vehicles):\n",
     "        current_node = 0\n",
-    "        plan_output = f'Route for vehicle {v}:\\n'\n",
+    "        plan_output = f\"Route for vehicle {v}:\\n\"\n",
     "        route_distance = 0\n",
     "        value_collected = 0\n",
     "        route_is_finished = False\n",
     "        while not route_is_finished:\n",
     "            value_collected += VISIT_VALUES[current_node]\n",
-    "            plan_output += f' {current_node} ->'\n",
+    "            plan_output += f\" {current_node} ->\"\n",
     "            # find next node\n",
     "            for node in range(num_nodes):\n",
     "                if node == current_node:\n",
@@ -172,21 +174,21 @@
     "                    if current_node == 0:\n",
     "                        route_is_finished = True\n",
     "                    break\n",
-    "        plan_output += f' {current_node}\\n'\n",
-    "        plan_output += f'Distance of the route: {route_distance}m\\n'\n",
-    "        plan_output += f'Value collected: {value_collected}\\n'\n",
+    "        plan_output += f\" {current_node}\\n\"\n",
+    "        plan_output += f\"Distance of the route: {route_distance}m\\n\"\n",
+    "        plan_output += f\"Value collected: {value_collected}\\n\"\n",
     "        print(plan_output)\n",
     "        total_distance += route_distance\n",
     "        total_value_collected += value_collected\n",
-    "    print(f'Total Distance: {total_distance}m')\n",
-    "    print(f'Total Value collected: {total_value_collected}/{sum(VISIT_VALUES)}')\n",
+    "    print(f\"Total Distance: {total_distance}m\")\n",
+    "    print(f\"Total Value collected: {total_value_collected}/{sum(VISIT_VALUES)}\")\n",
     "\n",
     "\n",
     "def prize_collecting_vrp():\n",
     "    \"\"\"Entry point of the program.\"\"\"\n",
     "    num_nodes = len(DISTANCE_MATRIX)\n",
     "    num_vehicles = 4\n",
-    "    print(f'Num nodes = {num_nodes}')\n",
+    "    print(f\"Num nodes = {num_nodes}\")\n",
     "\n",
     "    # Model.\n",
     "    model = cp_model.CpModel()\n",
@@ -203,8 +205,8 @@
     "        used_arcs[v] = {}\n",
     "        arcs = []\n",
     "        for i in all_nodes:\n",
-    "            is_visited = model.NewBoolVar(f'{i} is visited')\n",
-    "            arcs.append([i, i, is_visited.Not()])\n",
+    "            is_visited = model.NewBoolVar(f\"{i} is visited\")\n",
+    "            arcs.append((i, i, is_visited.Not()))\n",
     "\n",
     "            obj_vars.append(is_visited)\n",
     "            obj_coeffs.append(VISIT_VALUES[i])\n",
@@ -214,8 +216,8 @@
     "                if i == j:\n",
     "                    used_arcs[v][i, j] = is_visited.Not()\n",
     "                    continue\n",
-    "                arc_is_used = model.NewBoolVar(f'{j} follows {i}')\n",
-    "                arcs.append([i, j, arc_is_used])\n",
+    "                arc_is_used = model.NewBoolVar(f\"{j} follows {i}\")\n",
+    "                arcs.append((i, j, arc_is_used))\n",
     "\n",
     "                obj_vars.append(arc_is_used)\n",
     "                obj_coeffs.append(-DISTANCE_MATRIX[i][j])\n",
@@ -228,18 +230,20 @@
     "\n",
     "        # limit the route distance\n",
     "        model.Add(\n",
-    "            sum(used_arcs[v][i, j] * DISTANCE_MATRIX[i][j]\n",
+    "            sum(\n",
+    "                used_arcs[v][i, j] * DISTANCE_MATRIX[i][j]\n",
     "                for i in all_nodes\n",
-    "                for j in all_nodes) <= MAX_DISTANCE)\n",
+    "                for j in all_nodes\n",
+    "            )\n",
+    "            <= MAX_DISTANCE\n",
+    "        )\n",
     "\n",
     "    # Each node is visited at most once\n",
     "    for node in range(1, num_nodes):\n",
-    "        model.AddAtMostOne(\n",
-    "            [visited_nodes[v][node] for v in range(num_vehicles)])\n",
+    "        model.AddAtMostOne([visited_nodes[v][node] for v in range(num_vehicles)])\n",
     "\n",
     "    # Maximize visited node values minus the travelled distance.\n",
-    "    model.Maximize(\n",
-    "        sum(obj_vars[i] * obj_coeffs[i] for i in range(len(obj_vars))))\n",
+    "    model.Maximize(sum(obj_vars[i] * obj_coeffs[i] for i in range(len(obj_vars))))\n",
     "\n",
     "    # Solve and print out the solution.\n",
     "    solver = cp_model.CpSolver()\n",
@@ -249,13 +253,12 @@
     "\n",
     "    status = solver.Solve(model)\n",
     "    if status == cp_model.FEASIBLE or status == cp_model.OPTIMAL:\n",
-    "        print_solution(solver, visited_nodes, used_arcs, num_nodes,\n",
-    "                       num_vehicles)\n",
+    "        print_solution(solver, visited_nodes, used_arcs, num_nodes, num_vehicles)\n",
     "\n",
     "\n",
     "def main(argv: Sequence[str]) -> None:\n",
     "    if len(argv) > 1:\n",
-    "        raise app.UsageError('Too many command-line arguments.')\n",
+    "        raise app.UsageError(\"Too many command-line arguments.\")\n",
     "    prize_collecting_vrp()\n",
     "\n",
     "\n",
diff --git a/examples/notebook/examples/pyflow_example.ipynb b/examples/notebook/examples/pyflow_example.ipynb
index 7a86a8f870..f2ac46bca0 100644
--- a/examples/notebook/examples/pyflow_example.ipynb
+++ b/examples/notebook/examples/pyflow_example.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "MaxFlow and MinCostFlow examples.\n"
    ]
   },
@@ -89,7 +90,7 @@
     "\n",
     "def max_flow_api():\n",
     "    \"\"\"MaxFlow simple interface example.\"\"\"\n",
-    "    print('MaxFlow on a simple network.')\n",
+    "    print(\"MaxFlow on a simple network.\")\n",
     "    tails = [0, 0, 0, 0, 1, 2, 3, 3, 4]\n",
     "    heads = [1, 2, 3, 4, 3, 4, 4, 5, 5]\n",
     "    capacities = [5, 8, 5, 3, 4, 5, 6, 6, 4]\n",
@@ -98,52 +99,54 @@
     "    for i in range(0, len(tails)):\n",
     "        smf.add_arc_with_capacity(tails[i], heads[i], capacities[i])\n",
     "    if smf.solve(0, 5) == smf.OPTIMAL:\n",
-    "        print('Total flow', smf.optimal_flow(), '/', expected_total_flow)\n",
+    "        print(\"Total flow\", smf.optimal_flow(), \"/\", expected_total_flow)\n",
     "        for i in range(smf.num_arcs()):\n",
-    "            print('From source %d to target %d: %d / %d' %\n",
-    "                  (smf.tail(i), smf.head(i), smf.flow(i), smf.capacity(i)))\n",
-    "        print('Source side min-cut:', smf.get_source_side_min_cut())\n",
-    "        print('Sink side min-cut:', smf.get_sink_side_min_cut())\n",
+    "            print(\n",
+    "                \"From source %d to target %d: %d / %d\"\n",
+    "                % (smf.tail(i), smf.head(i), smf.flow(i), smf.capacity(i))\n",
+    "            )\n",
+    "        print(\"Source side min-cut:\", smf.get_source_side_min_cut())\n",
+    "        print(\"Sink side min-cut:\", smf.get_sink_side_min_cut())\n",
     "    else:\n",
-    "        print('There was an issue with the max flow input.')\n",
+    "        print(\"There was an issue with the max flow input.\")\n",
     "\n",
     "\n",
     "def min_cost_flow_api():\n",
     "    \"\"\"MinCostFlow simple interface example.\n",
     "\n",
-    "  Note that this example is actually a linear sum assignment example and will\n",
-    "  be more efficiently solved with the pywrapgraph.LinearSumAssignment class.\n",
-    "  \"\"\"\n",
-    "    print('MinCostFlow on 4x4 matrix.')\n",
+    "    Note that this example is actually a linear sum assignment example and will\n",
+    "    be more efficiently solved with the pywrapgraph.LinearSumAssignment class.\n",
+    "    \"\"\"\n",
+    "    print(\"MinCostFlow on 4x4 matrix.\")\n",
     "    num_sources = 4\n",
     "    num_targets = 4\n",
-    "    costs = [[90, 75, 75, 80], [35, 85, 55, 65], [125, 95, 90, 105],\n",
-    "             [45, 110, 95, 115]]\n",
+    "    costs = [[90, 75, 75, 80], [35, 85, 55, 65], [125, 95, 90, 105], [45, 110, 95, 115]]\n",
     "    expected_cost = 275\n",
     "    smcf = min_cost_flow.SimpleMinCostFlow()\n",
     "    for source in range(0, num_sources):\n",
     "        for target in range(0, num_targets):\n",
-    "            smcf.add_arc_with_capacity_and_unit_cost(source,\n",
-    "                                                     num_sources + target, 1,\n",
-    "                                                     costs[source][target])\n",
+    "            smcf.add_arc_with_capacity_and_unit_cost(\n",
+    "                source, num_sources + target, 1, costs[source][target]\n",
+    "            )\n",
     "    for node in range(0, num_sources):\n",
     "        smcf.set_node_supply(node, 1)\n",
     "        smcf.set_node_supply(num_sources + node, -1)\n",
     "    status = smcf.solve()\n",
     "    if status == smcf.OPTIMAL:\n",
-    "        print('Total flow', smcf.optimal_cost(), '/', expected_cost)\n",
+    "        print(\"Total flow\", smcf.optimal_cost(), \"/\", expected_cost)\n",
     "        for i in range(0, smcf.num_arcs()):\n",
     "            if smcf.flow(i) > 0:\n",
-    "                print('From source %d to target %d: cost %d' %\n",
-    "                      (smcf.tail(i), smcf.head(i) - num_sources,\n",
-    "                       smcf.unit_cost(i)))\n",
+    "                print(\n",
+    "                    \"From source %d to target %d: cost %d\"\n",
+    "                    % (smcf.tail(i), smcf.head(i) - num_sources, smcf.unit_cost(i))\n",
+    "                )\n",
     "    else:\n",
-    "        print('There was an issue with the min cost flow input.')\n",
+    "        print(\"There was an issue with the min cost flow input.\")\n",
     "\n",
     "\n",
     "def main(argv: Sequence[str]) -> None:\n",
     "    if len(argv) > 1:\n",
-    "        raise app.UsageError('Too many command-line arguments.')\n",
+    "        raise app.UsageError(\"Too many command-line arguments.\")\n",
     "    max_flow_api()\n",
     "    min_cost_flow_api()\n",
     "\n",
diff --git a/examples/notebook/examples/qubo_sat.ipynb b/examples/notebook/examples/qubo_sat.ipynb
index 551f2a39f2..b3401652ff 100644
--- a/examples/notebook/examples/qubo_sat.ipynb
+++ b/examples/notebook/examples/qubo_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Solves a Qubo program using the CP-SAT solver.\n"
    ]
   },
@@ -86,6 +87,7 @@
     "from ortools.sat.python import cp_model\n",
     "\n",
     "RAW_DATA = [\n",
+    "    # fmt:off\n",
     "    [\n",
     "        0, 0, 49.774821, -59.5968886, -46.0773896, 0, -65.166109, 0, 0, 0, 0, 0,\n",
     "        0, 0, 0, 0, 0, 47.0957778, 15.259961, -98.7983264, 0, 0, 0, -20.7757184,\n",
@@ -714,6 +716,7 @@
     "        0, 84.8495464, 0, 0, 0, 0, 0, 0, 33.0825986, 46.995148, 0, 0, 0,\n",
     "        -4.243203, 0, 0, -24.0124188\n",
     "    ]\n",
+    "    # fmt:on\n",
     "]\n",
     "\n",
     "\n",
@@ -725,7 +728,7 @@
     "\n",
     "    num_vars = len(RAW_DATA)\n",
     "    all_vars = range(num_vars)\n",
-    "    variables = [model.NewBoolVar('x_%i' % i) for i in all_vars]\n",
+    "    variables = [model.NewBoolVar(\"x_%i\" % i) for i in all_vars]\n",
     "\n",
     "    obj_vars = []\n",
     "    obj_coeffs = []\n",
@@ -737,7 +740,7 @@
     "            if coeff == 0.0:\n",
     "                continue\n",
     "            x_j = variables[j]\n",
-    "            var = model.NewBoolVar('')\n",
+    "            var = model.NewBoolVar(\"\")\n",
     "            model.AddBoolOr([x_i.Not(), x_j.Not(), var])\n",
     "            model.AddImplication(var, x_i)\n",
     "            model.AddImplication(var, x_j)\n",
@@ -750,8 +753,7 @@
     "            obj_vars.append(variables[i])\n",
     "            obj_coeffs.append(self_coeff)\n",
     "\n",
-    "    model.Minimize(\n",
-    "        sum(obj_vars[i] * obj_coeffs[i] for i in range(len(obj_vars))))\n",
+    "    model.Minimize(sum(obj_vars[i] * obj_coeffs[i] for i in range(len(obj_vars))))\n",
     "\n",
     "    ### Solve model.\n",
     "    solver = cp_model.CpSolver()\n",
@@ -763,7 +765,7 @@
     "\n",
     "def main(argv: Sequence[str]) -> None:\n",
     "    if len(argv) > 1:\n",
-    "        raise app.UsageError('Too many command-line arguments.')\n",
+    "        raise app.UsageError(\"Too many command-line arguments.\")\n",
     "    solve_qubo()\n",
     "\n",
     "\n",
diff --git a/examples/notebook/examples/rcpsp_sat.ipynb b/examples/notebook/examples/rcpsp_sat.ipynb
index 308613978c..0df5a89f65 100644
--- a/examples/notebook/examples/rcpsp_sat.ipynb
+++ b/examples/notebook/examples/rcpsp_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Sat based solver for the RCPSP problems (see rcpsp.proto).\n",
     "\n",
     "Introduction to the problem:\n",
@@ -94,39 +95,47 @@
     "\n",
     "from google.protobuf import text_format\n",
     "from ortools.sat.python import cp_model\n",
-    "from ortools.scheduling import pywraprcpsp\n",
     "from ortools.scheduling import rcpsp_pb2\n",
+    "from ortools.scheduling.python import rcpsp\n",
     "\n",
-    "class FLAGS: pass\n",
-    "\n",
-    "_INPUT = flags.DEFINE_string('input', '', 'Input file to parse and solve.')\n",
+    "_INPUT = flags.DEFINE_string(\"input\", \"\", \"Input file to parse and solve.\")\n",
     "_OUTPUT_PROTO = flags.DEFINE_string(\n",
-    "    'output_proto', '', 'Output file to write the cp_model proto to.')\n",
-    "_PARAMS = flags.DEFINE_string('params', '', 'Sat solver parameters.')\n",
+    "    \"output_proto\", \"\", \"Output file to write the cp_model proto to.\"\n",
+    ")\n",
+    "_PARAMS = flags.DEFINE_string(\"params\", \"\", \"Sat solver parameters.\")\n",
     "_USE_INTERVAL_MAKESPAN = flags.DEFINE_bool(\n",
-    "    'use_interval_makespan', True,\n",
-    "    'Whether we encode the makespan using an interval or not.')\n",
-    "_HORIZON = flags.DEFINE_integer('horizon', -1, 'Force horizon.')\n",
+    "    \"use_interval_makespan\",\n",
+    "    False,\n",
+    "    \"Whether we encode the makespan using an interval or not.\",\n",
+    ")\n",
+    "_HORIZON = flags.DEFINE_integer(\"horizon\", -1, \"Force horizon.\")\n",
     "_ADD_REDUNDANT_ENERGETIC_CONSTRAINTS = flags.DEFINE_bool(\n",
-    "    'add_redundant_energetic_constraints', False,\n",
-    "    'Add redundant energetic constraints on the pairs of tasks extracted from' +\n",
-    "    ' precedence graph.')\n",
+    "    \"add_redundant_energetic_constraints\",\n",
+    "    False,\n",
+    "    \"Add redundant energetic constraints on the pairs of tasks extracted from\"\n",
+    "    + \" precedence graph.\",\n",
+    ")\n",
     "_DELAY_TIME_LIMIT = flags.DEFINE_float(\n",
-    "    'delay_time_limit', 0.0,\n",
-    "    'Time limit when computing min delay between tasks.' +\n",
-    "    ' A non-positive time limit disable min delays computation.')\n",
+    "    \"delay_time_limit\",\n",
+    "    0.0,\n",
+    "    \"Time limit when computing min delay between tasks.\"\n",
+    "    + \" A non-positive time limit disable min delays computation.\",\n",
+    ")\n",
     "_PREEMPTIVE_LB_TIME_LIMIT = flags.DEFINE_float(\n",
-    "    'preemptive_lb_time_limit', 0.0,\n",
-    "    'Time limit when computing a preemptive schedule lower bound.' +\n",
-    "    ' A non-positive time limit disable this computation.')\n",
+    "    \"preemptive_lb_time_limit\",\n",
+    "    0.0,\n",
+    "    \"Time limit when computing a preemptive schedule lower bound.\"\n",
+    "    + \" A non-positive time limit disable this computation.\",\n",
+    ")\n",
     "\n",
     "\n",
     "def PrintProblemStatistics(problem):\n",
     "    \"\"\"Display various statistics on the problem.\"\"\"\n",
     "\n",
     "    # Determine problem type.\n",
-    "    problem_type = ('Resource Investment Problem'\n",
-    "                    if problem.is_resource_investment else 'RCPSP')\n",
+    "    problem_type = (\n",
+    "        \"Resource Investment Problem\" if problem.is_resource_investment else \"RCPSP\"\n",
+    "    )\n",
     "\n",
     "    num_resources = len(problem.resources)\n",
     "    num_tasks = len(problem.tasks) - 2  # 2 sentinels.\n",
@@ -146,43 +155,39 @@
     "            tasks_with_delay += 1\n",
     "\n",
     "    if problem.is_rcpsp_max:\n",
-    "        problem_type += '/Max delay'\n",
+    "        problem_type += \"/Max delay\"\n",
     "    # We print 2 less tasks as these are sentinel tasks that are not counted in\n",
     "    # the description of the rcpsp models.\n",
     "    if problem.is_consumer_producer:\n",
-    "        print(f'Solving {problem_type} with:')\n",
-    "        print(f'  - {num_resources} reservoir resources')\n",
-    "        print(f'  - {num_tasks} tasks')\n",
+    "        print(f\"Solving {problem_type} with:\")\n",
+    "        print(f\"  - {num_resources} reservoir resources\")\n",
+    "        print(f\"  - {num_tasks} tasks\")\n",
     "    else:\n",
-    "        print(f'Solving {problem_type} with:')\n",
-    "        print(f'  - {num_resources} renewable resources')\n",
-    "        print(f'  - {num_tasks} tasks')\n",
+    "        print(f\"Solving {problem_type} with:\")\n",
+    "        print(f\"  - {num_resources} renewable resources\")\n",
+    "        print(f\"  - {num_tasks} tasks\")\n",
     "        if tasks_with_alternatives:\n",
-    "            print(\n",
-    "                f'    - {tasks_with_alternatives} tasks with alternative resources'\n",
-    "            )\n",
+    "            print(f\"    - {tasks_with_alternatives} tasks with alternative resources\")\n",
     "        if variable_duration_tasks:\n",
-    "            print(\n",
-    "                f'    - {variable_duration_tasks} tasks with variable durations'\n",
-    "            )\n",
+    "            print(f\"    - {variable_duration_tasks} tasks with variable durations\")\n",
     "        if tasks_with_delay:\n",
-    "            print(f'    - {tasks_with_delay} tasks with successor delays')\n",
+    "            print(f\"    - {tasks_with_delay} tasks with successor delays\")\n",
     "\n",
     "\n",
     "def AnalyseDependencyGraph(problem):\n",
     "    \"\"\"Analyses the dependency graph to improve the model.\n",
     "\n",
-    "  Args:\n",
-    "    problem: the protobuf of the problem to solve.\n",
+    "    Args:\n",
+    "      problem: the protobuf of the problem to solve.\n",
     "\n",
-    "  Returns:\n",
-    "    a list of (task1, task2, in_between_tasks) with task2 and indirect successor\n",
-    "    of task1, and in_between_tasks being the list of all tasks after task1 and\n",
-    "    before task2.\n",
-    "  \"\"\"\n",
+    "    Returns:\n",
+    "      a list of (task1, task2, in_between_tasks) with task2 and indirect successor\n",
+    "      of task1, and in_between_tasks being the list of all tasks after task1 and\n",
+    "      before task2.\n",
+    "    \"\"\"\n",
     "\n",
     "    num_nodes = len(problem.tasks)\n",
-    "    print(f'Analysing the dependency graph over {num_nodes} nodes')\n",
+    "    print(f\"Analysing the dependency graph over {num_nodes} nodes\")\n",
     "\n",
     "    ins = collections.defaultdict(list)\n",
     "    outs = collections.defaultdict(list)\n",
@@ -239,49 +244,50 @@
     "\n",
     "    # Sort entries lexicographically by (len(common), source, sink)\n",
     "    def Price(entry):\n",
-    "        return (num_nodes * num_nodes * len(entry[2]) + num_nodes * entry[0] +\n",
-    "                entry[1])\n",
+    "        return num_nodes * num_nodes * len(entry[2]) + num_nodes * entry[0] + entry[1]\n",
     "\n",
     "    result.sort(key=Price)\n",
-    "    print(f'  - created {len(result)} pairs of nodes to examine', flush=True)\n",
+    "    print(f\"  - created {len(result)} pairs of nodes to examine\", flush=True)\n",
     "    return result, after\n",
     "\n",
     "\n",
-    "def SolveRcpsp(problem,\n",
-    "               proto_file,\n",
-    "               params,\n",
-    "               active_tasks,\n",
-    "               source,\n",
-    "               sink,\n",
-    "               intervals_of_tasks,\n",
-    "               delays,\n",
-    "               in_main_solve=False,\n",
-    "               initial_solution=None,\n",
-    "               lower_bound=0):\n",
+    "def SolveRcpsp(\n",
+    "    problem,\n",
+    "    proto_file,\n",
+    "    params,\n",
+    "    active_tasks,\n",
+    "    source,\n",
+    "    sink,\n",
+    "    intervals_of_tasks,\n",
+    "    delays,\n",
+    "    in_main_solve=False,\n",
+    "    initial_solution=None,\n",
+    "    lower_bound=0,\n",
+    "):\n",
     "    \"\"\"Parse and solve a given RCPSP problem in proto format.\n",
     "\n",
-    "  The model will only look at the tasks {source} + {sink} + active_tasks, and\n",
-    "  ignore all others.\n",
+    "    The model will only look at the tasks {source} + {sink} + active_tasks, and\n",
+    "    ignore all others.\n",
     "\n",
-    "  Args:\n",
-    "    problem: the description of the model to solve in protobuf format\n",
-    "    proto_file: the name of the file to export the CpModel proto to.\n",
-    "    params: the string representation of the parameters to pass to the sat\n",
-    "      solver.\n",
-    "    active_tasks: the set of active tasks to consider.\n",
-    "    source: the source task in the graph. Its end will be forced to 0.\n",
-    "    sink: the sink task of the graph. Its start is the makespan of the problem.\n",
-    "    intervals_of_tasks: a heuristic lists of (task1, task2, tasks) used to add\n",
-    "      redundant energetic equations to the model.\n",
-    "    delays: a list of (task1, task2, min_delays) used to add extended precedence\n",
-    "      constraints (start(task2) >= end(task1) + min_delay).\n",
-    "    in_main_solve: indicates if this is the main solve procedure.\n",
-    "    initial_solution: A valid assignment used to hint the search.\n",
-    "    lower_bound: A valid lower bound of the makespan objective.\n",
+    "    Args:\n",
+    "      problem: the description of the model to solve in protobuf format\n",
+    "      proto_file: the name of the file to export the CpModel proto to.\n",
+    "      params: the string representation of the parameters to pass to the sat\n",
+    "        solver.\n",
+    "      active_tasks: the set of active tasks to consider.\n",
+    "      source: the source task in the graph. Its end will be forced to 0.\n",
+    "      sink: the sink task of the graph. Its start is the makespan of the problem.\n",
+    "      intervals_of_tasks: a heuristic lists of (task1, task2, tasks) used to add\n",
+    "        redundant energetic equations to the model.\n",
+    "      delays: a list of (task1, task2, min_delays) used to add extended precedence\n",
+    "        constraints (start(task2) >= end(task1) + min_delay).\n",
+    "      in_main_solve: indicates if this is the main solve procedure.\n",
+    "      initial_solution: A valid assignment used to hint the search.\n",
+    "      lower_bound: A valid lower bound of the makespan objective.\n",
     "\n",
-    "  Returns:\n",
-    "    (lower_bound of the objective, best solution found, asssignment)\n",
-    "  \"\"\"\n",
+    "    Returns:\n",
+    "      (lower_bound of the objective, best solution found, assignment)\n",
+    "    \"\"\"\n",
     "    # Create the model.\n",
     "    model = cp_model.CpModel()\n",
     "    model.SetName(problem.name)\n",
@@ -306,7 +312,7 @@
     "                        for d in rd.min_delays:\n",
     "                            horizon += abs(d)\n",
     "    if in_main_solve:\n",
-    "        print(f'Horizon = {horizon}', flush=True)\n",
+    "        print(f\"Horizon = {horizon}\", flush=True)\n",
     "\n",
     "    # Containers.\n",
     "    task_starts = {}\n",
@@ -329,15 +335,13 @@
     "        num_recipes = len(task.recipes)\n",
     "        all_recipes = range(num_recipes)\n",
     "\n",
-    "        start_var = model.NewIntVar(0, horizon, f'start_of_task_{t}')\n",
-    "        end_var = model.NewIntVar(0, horizon, f'end_of_task_{t}')\n",
+    "        start_var = model.NewIntVar(0, horizon, f\"start_of_task_{t}\")\n",
+    "        end_var = model.NewIntVar(0, horizon, f\"end_of_task_{t}\")\n",
     "\n",
     "        literals = []\n",
     "        if num_recipes > 1:\n",
     "            # Create one literal per recipe.\n",
-    "            literals = [\n",
-    "                model.NewBoolVar(f'is_present_{t}_{r}') for r in all_recipes\n",
-    "            ]\n",
+    "            literals = [model.NewBoolVar(f\"is_present_{t}_{r}\") for r in all_recipes]\n",
     "\n",
     "            # Exactly one recipe must be performed.\n",
     "            model.AddExactlyOne(literals)\n",
@@ -357,17 +361,19 @@
     "        # Create the duration variable from the accumulated durations.\n",
     "        duration_var = model.NewIntVarFromDomain(\n",
     "            cp_model.Domain.FromValues(task_to_recipe_durations[t]),\n",
-    "            f'duration_of_task_{t}')\n",
+    "            f\"duration_of_task_{t}\",\n",
+    "        )\n",
     "\n",
     "        # Link the recipe literals and the duration_var.\n",
     "        for r in range(num_recipes):\n",
-    "            model.Add(\n",
-    "                duration_var == task_to_recipe_durations[t][r]).OnlyEnforceIf(\n",
-    "                    literals[r])\n",
+    "            model.Add(duration_var == task_to_recipe_durations[t][r]).OnlyEnforceIf(\n",
+    "                literals[r]\n",
+    "            )\n",
     "\n",
     "        # Create the interval of the task.\n",
-    "        task_interval = model.NewIntervalVar(start_var, duration_var, end_var,\n",
-    "                                             f'task_interval_{t}')\n",
+    "        task_interval = model.NewIntervalVar(\n",
+    "            start_var, duration_var, end_var, f\"task_interval_{t}\"\n",
+    "        )\n",
     "\n",
     "        # Store task variables.\n",
     "        task_starts[t] = start_var\n",
@@ -381,33 +387,38 @@
     "            demands = [demand_matrix[(res, recipe)] for recipe in all_recipes]\n",
     "            task_resource_to_fixed_demands[(t, res)] = demands\n",
     "            demand_var = model.NewIntVarFromDomain(\n",
-    "                cp_model.Domain.FromValues(demands), f'demand_{t}_{res}')\n",
+    "                cp_model.Domain.FromValues(demands), f\"demand_{t}_{res}\"\n",
+    "            )\n",
     "            task_to_resource_demands[t].append(demand_var)\n",
     "\n",
     "            # Link the recipe literals and the demand_var.\n",
     "            for r in all_recipes:\n",
     "                model.Add(demand_var == demand_matrix[(res, r)]).OnlyEnforceIf(\n",
-    "                    literals[r])\n",
+    "                    literals[r]\n",
+    "                )\n",
     "\n",
     "            resource_to_sum_of_demand_max[res] += max(demands)\n",
     "\n",
     "        # Create the energy expression for (task, resource):\n",
     "        for res in all_resources:\n",
     "            task_resource_to_energy[(t, res)] = sum(\n",
-    "                literals[r] * task_to_recipe_durations[t][r] *\n",
-    "                task_resource_to_fixed_demands[(t, res)][r]\n",
-    "                for r in all_recipes)\n",
+    "                literals[r]\n",
+    "                * task_to_recipe_durations[t][r]\n",
+    "                * task_resource_to_fixed_demands[(t, res)][r]\n",
+    "                for r in all_recipes\n",
+    "            )\n",
     "            task_resource_to_max_energy[(t, res)] = max(\n",
-    "                task_to_recipe_durations[t][r] *\n",
-    "                task_resource_to_fixed_demands[(t, res)][r]\n",
-    "                for r in all_recipes)\n",
+    "                task_to_recipe_durations[t][r]\n",
+    "                * task_resource_to_fixed_demands[(t, res)][r]\n",
+    "                for r in all_recipes\n",
+    "            )\n",
     "\n",
     "    # Create makespan variable\n",
-    "    makespan = model.NewIntVar(lower_bound, horizon, 'makespan')\n",
-    "    makespan_size = model.NewIntVar(1, horizon, 'interval_makespan_size')\n",
-    "    interval_makespan = model.NewIntervalVar(makespan, makespan_size,\n",
-    "                                             model.NewConstant(horizon + 1),\n",
-    "                                             'interval_makespan')\n",
+    "    makespan = model.NewIntVar(lower_bound, horizon, \"makespan\")\n",
+    "    makespan_size = model.NewIntVar(1, horizon, \"interval_makespan_size\")\n",
+    "    interval_makespan = model.NewIntervalVar(\n",
+    "        makespan, makespan_size, model.NewConstant(horizon + 1), \"interval_makespan\"\n",
+    "    )\n",
     "\n",
     "    # Add precedences.\n",
     "    if problem.is_rcpsp_max:\n",
@@ -417,8 +428,7 @@
     "            task = problem.tasks[task_id]\n",
     "            num_modes = len(task.recipes)\n",
     "\n",
-    "            for successor_index in range(len(task.successors)):\n",
-    "                next_id = task.successors[successor_index]\n",
+    "            for successor_index, next_id in enumerate(task.successors):\n",
     "                delay_matrix = task.successor_delays[successor_index]\n",
     "                num_next_modes = len(problem.tasks[next_id].recipes)\n",
     "                for m1 in range(num_modes):\n",
@@ -429,8 +439,7 @@
     "                        model.Add(s1 + delay <= makespan).OnlyEnforceIf(p1)\n",
     "                    else:\n",
     "                        for m2 in range(num_next_modes):\n",
-    "                            delay = delay_matrix.recipe_delays[m1].min_delays[\n",
-    "                                m2]\n",
+    "                            delay = delay_matrix.recipe_delays[m1].min_delays[m2]\n",
     "                            s2 = task_starts[next_id]\n",
     "                            p2 = task_to_presence_literals[next_id][m2]\n",
     "                            model.Add(s1 + delay <= s2).OnlyEnforceIf([p1, p2])\n",
@@ -452,18 +461,16 @@
     "        resource = problem.resources[res]\n",
     "        c = resource.max_capacity\n",
     "        if c == -1:\n",
-    "            print(f'No capacity: {resource}')\n",
+    "            print(f\"No capacity: {resource}\")\n",
     "            c = resource_to_sum_of_demand_max[res]\n",
     "\n",
     "        # RIP problems have only renewable resources, and no makespan.\n",
     "        if problem.is_resource_investment or resource.renewable:\n",
     "            intervals = [task_intervals[t] for t in all_active_tasks]\n",
-    "            demands = [\n",
-    "                task_to_resource_demands[t][res] for t in all_active_tasks\n",
-    "            ]\n",
+    "            demands = [task_to_resource_demands[t][res] for t in all_active_tasks]\n",
     "\n",
     "            if problem.is_resource_investment:\n",
-    "                capacity = model.NewIntVar(0, c, f'capacity_of_{res}')\n",
+    "                capacity = model.NewIntVar(0, c, f\"capacity_of_{res}\")\n",
     "                model.AddCumulative(intervals, demands, capacity)\n",
     "                capacities.append(capacity)\n",
     "                max_cost += c * resource.unit_cost\n",
@@ -481,24 +488,32 @@
     "                    if task_resource_to_fixed_demands[(t, res)][0]:\n",
     "                        reservoir_starts.append(task_starts[t])\n",
     "                        reservoir_demands.append(\n",
-    "                            task_resource_to_fixed_demands[(t, res)][0])\n",
-    "                model.AddReservoirConstraint(reservoir_starts,\n",
-    "                                             reservoir_demands,\n",
-    "                                             resource.min_capacity,\n",
-    "                                             resource.max_capacity)\n",
+    "                            task_resource_to_fixed_demands[(t, res)][0]\n",
+    "                        )\n",
+    "                model.AddReservoirConstraint(\n",
+    "                    reservoir_starts,\n",
+    "                    reservoir_demands,\n",
+    "                    resource.min_capacity,\n",
+    "                    resource.max_capacity,\n",
+    "                )\n",
     "            else:  # No producer-consumer. We just sum the demands.\n",
     "                model.Add(\n",
-    "                    cp_model.LinearExpr.Sum([\n",
-    "                        task_to_resource_demands[t][res]\n",
-    "                        for t in all_active_tasks\n",
-    "                    ]) <= c)\n",
+    "                    cp_model.LinearExpr.Sum(\n",
+    "                        [task_to_resource_demands[t][res] for t in all_active_tasks]\n",
+    "                    )\n",
+    "                    <= c\n",
+    "                )\n",
     "\n",
     "    # Objective.\n",
     "    if problem.is_resource_investment:\n",
-    "        objective = model.NewIntVar(0, max_cost, 'capacity_costs')\n",
-    "        model.Add(objective == sum(problem.resources[i].unit_cost *\n",
-    "                                   capacities[i]\n",
-    "                                   for i in range(len(capacities))))\n",
+    "        objective = model.NewIntVar(0, max_cost, \"capacity_costs\")\n",
+    "        model.Add(\n",
+    "            objective\n",
+    "            == sum(\n",
+    "                problem.resources[i].unit_cost * capacities[i]\n",
+    "                for i in range(len(capacities))\n",
+    "            )\n",
+    "        )\n",
     "    else:\n",
     "        objective = makespan\n",
     "\n",
@@ -512,8 +527,7 @@
     "            elif local_start in active_tasks and local_end == sink:\n",
     "                model.Add(makespan >= task_ends[local_start] + min_delay)\n",
     "            elif local_start in active_tasks and local_end in active_tasks:\n",
-    "                model.Add(task_starts[local_end] >= task_ends[local_start] +\n",
-    "                          min_delay)\n",
+    "                model.Add(task_starts[local_end] >= task_ends[local_start] + min_delay)\n",
     "\n",
     "    problem_is_single_mode = True\n",
     "    for t in all_active_tasks:\n",
@@ -533,10 +547,13 @@
     "    # graph, it add the energetic relaxation:\n",
     "    #   (start_var('end') - end_var('start')) * capacity_max >=\n",
     "    #        sum of linearized energies of all tasks from 'in_between_tasks'\n",
-    "    if (not problem.is_resource_investment and\n",
-    "            not problem.is_consumer_producer and\n",
-    "            _ADD_REDUNDANT_ENERGETIC_CONSTRAINTS.value and in_main_solve and\n",
-    "            not problem_is_single_mode):\n",
+    "    if (\n",
+    "        not problem.is_resource_investment\n",
+    "        and not problem.is_consumer_producer\n",
+    "        and _ADD_REDUNDANT_ENERGETIC_CONSTRAINTS.value\n",
+    "        and in_main_solve\n",
+    "        and not problem_is_single_mode\n",
+    "    ):\n",
     "        added_constraints = 0\n",
     "        ignored_constraits = 0\n",
     "        for local_start, local_end, common in intervals_of_tasks:\n",
@@ -548,19 +565,21 @@
     "                if delays and (local_start, local_end) in delays:\n",
     "                    min_delay, _ = delays[local_start, local_end]\n",
     "                    sum_of_max_energies = sum(\n",
-    "                        task_resource_to_max_energy[(t, res)] for t in common)\n",
+    "                        task_resource_to_max_energy[(t, res)] for t in common\n",
+    "                    )\n",
     "                    if sum_of_max_energies <= c * min_delay:\n",
     "                        ignored_constraits += 1\n",
     "                        continue\n",
     "                model.Add(\n",
-    "                    c *\n",
-    "                    (task_starts[local_end] - task_ends[local_start]) >= sum(\n",
-    "                        task_resource_to_energy[(t, res)] for t in common))\n",
+    "                    c * (task_starts[local_end] - task_ends[local_start])\n",
+    "                    >= sum(task_resource_to_energy[(t, res)] for t in common)\n",
+    "                )\n",
     "                added_constraints += 1\n",
     "        print(\n",
-    "            f'Added {added_constraints} redundant energetic constraints, and ' +\n",
-    "            f'ignored {ignored_constraits} constraints.',\n",
-    "            flush=True)\n",
+    "            f\"Added {added_constraints} redundant energetic constraints, and \"\n",
+    "            + f\"ignored {ignored_constraits} constraints.\",\n",
+    "            flush=True,\n",
+    "        )\n",
     "\n",
     "    # Add solution hint.\n",
     "    if initial_solution:\n",
@@ -572,19 +591,25 @@
     "\n",
     "    # Write model to file.\n",
     "    if proto_file:\n",
-    "        print(f'Writing proto to{proto_file}')\n",
+    "        print(f\"Writing proto to{proto_file}\")\n",
     "        model.ExportToFile(proto_file)\n",
     "\n",
     "    # Solve model.\n",
     "    solver = cp_model.CpSolver()\n",
+    "    if not _USE_INTERVAL_MAKESPAN.value:\n",
+    "        solver.parameters.exploit_all_precedences = True\n",
+    "        solver.parameters.use_hard_precedences_in_cumulative = True\n",
     "    if params:\n",
     "        text_format.Parse(params, solver.parameters)\n",
+    "    if solver.parameters.num_workers >= 16 and solver.parameters.num_workers < 24:\n",
+    "        solver.parameters.ignore_subsolvers.append(\"objective_lb_search\")\n",
+    "        solver.parameters.extra_subsolvers.append(\"objective_shaving_search\")\n",
     "    if in_main_solve:\n",
     "        solver.parameters.log_search_progress = True\n",
     "    status = solver.Solve(model)\n",
     "    if status == cp_model.OPTIMAL or status == cp_model.FEASIBLE:\n",
     "        assignment = rcpsp_pb2.RcpspAssignment()\n",
-    "        for t in range(len(problem.tasks)):\n",
+    "        for t, _ in enumerate(problem.tasks):\n",
     "            if t in task_starts:\n",
     "                assignment.start_of_task.append(solver.Value(task_starts[t]))\n",
     "                for r in range(len(task_to_presence_literals[t])):\n",
@@ -594,25 +619,32 @@
     "            else:  # t is not an active task.\n",
     "                assignment.start_of_task.append(0)\n",
     "                assignment.selected_recipe_of_task.append(0)\n",
-    "        return (int(solver.BestObjectiveBound()), int(solver.ObjectiveValue()),\n",
-    "                assignment)\n",
+    "        return (\n",
+    "            int(solver.BestObjectiveBound()),\n",
+    "            int(solver.ObjectiveValue()),\n",
+    "            assignment,\n",
+    "        )\n",
     "    return -1, -1, None\n",
     "\n",
     "\n",
     "def ComputeDelaysBetweenNodes(problem, task_intervals):\n",
     "    \"\"\"Computes the min delays between all pairs of tasks in 'task_intervals'.\n",
     "\n",
-    "  Args:\n",
-    "    problem: The protobuf of the model.\n",
-    "    task_intervals: The output of the AnalysePrecedenceGraph().\n",
+    "    Args:\n",
+    "      problem: The protobuf of the model.\n",
+    "      task_intervals: The output of the AnalysePrecedenceGraph().\n",
     "\n",
-    "  Returns:\n",
-    "    a list of (task1, task2, min_delay_between_task1_and_task2)\n",
-    "  \"\"\"\n",
-    "    print('Computing the minimum delay between pairs of intervals')\n",
+    "    Returns:\n",
+    "      a list of (task1, task2, min_delay_between_task1_and_task2)\n",
+    "    \"\"\"\n",
+    "    print(\"Computing the minimum delay between pairs of intervals\")\n",
     "    delays = {}\n",
-    "    if (problem.is_resource_investment or problem.is_consumer_producer or\n",
-    "            problem.is_rcpsp_max or _DELAY_TIME_LIMIT.value <= 0.0):\n",
+    "    if (\n",
+    "        problem.is_resource_investment\n",
+    "        or problem.is_consumer_producer\n",
+    "        or problem.is_rcpsp_max\n",
+    "        or _DELAY_TIME_LIMIT.value <= 0.0\n",
+    "    ):\n",
     "        return delays, None, False\n",
     "\n",
     "    complete_problem_assignment = None\n",
@@ -621,9 +653,15 @@
     "    optimal_found = True\n",
     "    for start_task, end_task, active_tasks in task_intervals:\n",
     "        min_delay, feasible_delay, assignment = SolveRcpsp(\n",
-    "            problem, '',\n",
-    "            f'num_search_workers:16,max_time_in_seconds:{_DELAY_TIME_LIMIT.value}',\n",
-    "            active_tasks, start_task, end_task, [], delays)\n",
+    "            problem,\n",
+    "            \"\",\n",
+    "            f\"num_search_workers:16,max_time_in_seconds:{_DELAY_TIME_LIMIT.value}\",\n",
+    "            active_tasks,\n",
+    "            start_task,\n",
+    "            end_task,\n",
+    "            [],\n",
+    "            delays,\n",
+    "        )\n",
     "        if min_delay != -1:\n",
     "            delays[(start_task, end_task)] = min_delay, feasible_delay\n",
     "            if start_task == 0 and end_task == len(problem.tasks) - 1:\n",
@@ -636,9 +674,9 @@
     "            num_delays_not_found += 1\n",
     "            optimal_found = False\n",
     "\n",
-    "    print(f'  - #optimal delays = {num_optimal_delays}', flush=True)\n",
+    "    print(f\"  - #optimal delays = {num_optimal_delays}\", flush=True)\n",
     "    if num_delays_not_found:\n",
-    "        print(f'  - #not computed delays = {num_delays_not_found}', flush=True)\n",
+    "        print(f\"  - #not computed delays = {num_delays_not_found}\", flush=True)\n",
     "\n",
     "    return delays, complete_problem_assignment, optimal_found\n",
     "\n",
@@ -654,8 +692,10 @@
     "        resource = problem.resources[res]\n",
     "        if not resource.renewable:\n",
     "            continue\n",
-    "        if (sum(demand_map[(t, res)] for t in current) +\n",
-    "                demand_map[(candidate, res)] > resource.max_capacity):\n",
+    "        if (\n",
+    "            sum(demand_map[(t, res)] for t in current) + demand_map[(candidate, res)]\n",
+    "            > resource.max_capacity\n",
+    "        ):\n",
     "            return False\n",
     "\n",
     "    return True\n",
@@ -664,23 +704,26 @@
     "def ComputePreemptiveLowerBound(problem, after, lower_bound):\n",
     "    \"\"\"Computes a preemtive lower bound for the makespan statically.\n",
     "\n",
-    "  For this, it breaks all intervals into a set of intervals of size one.\n",
-    "  Then it will try to assign all of them in a minimum number of configurations.\n",
-    "  This is a standard complete set covering using column generation approach\n",
-    "  where each column is a possible combination of itervals of size one.\n",
+    "    For this, it breaks all intervals into a set of intervals of size one.\n",
+    "    Then it will try to assign all of them in a minimum number of configurations.\n",
+    "    This is a standard complete set covering using column generation approach\n",
+    "    where each column is a possible combination of itervals of size one.\n",
     "\n",
-    "  Args:\n",
-    "    problem: The probuf of the model.\n",
-    "    after: a task to list of task dict that contains all tasks after a given\n",
-    "      task.\n",
-    "    lower_bound: A valid lower bound of the problem. It can be 0.\n",
+    "    Args:\n",
+    "      problem: The probuf of the model.\n",
+    "      after: a task to list of task dict that contains all tasks after a given\n",
+    "        task.\n",
+    "      lower_bound: A valid lower bound of the problem. It can be 0.\n",
     "\n",
-    "  Returns:\n",
-    "    a valid lower bound of the problem.\n",
-    "  \"\"\"\n",
+    "    Returns:\n",
+    "      a valid lower bound of the problem.\n",
+    "    \"\"\"\n",
     "    # Check this is a single mode problem.\n",
-    "    if (problem.is_rcpsp_max or problem.is_resource_investment or\n",
-    "            problem.is_consumer_producer):\n",
+    "    if (\n",
+    "        problem.is_rcpsp_max\n",
+    "        or problem.is_resource_investment\n",
+    "        or problem.is_consumer_producer\n",
+    "    ):\n",
     "        return lower_bound\n",
     "\n",
     "    demand_map = collections.defaultdict(int)\n",
@@ -700,9 +743,11 @@
     "            max_duration = max(max_duration, recipe.duration)\n",
     "            sum_of_demands += demand\n",
     "\n",
-    "    print(f'Compute a bin-packing lower bound with {len(all_active_tasks)}' +\n",
-    "          ' active tasks',\n",
-    "          flush=True)\n",
+    "    print(\n",
+    "        f\"Compute a bin-packing lower bound with {len(all_active_tasks)}\"\n",
+    "        + \" active tasks\",\n",
+    "        flush=True,\n",
+    "    )\n",
     "    all_combinations = []\n",
     "\n",
     "    for t in all_active_tasks:\n",
@@ -714,7 +759,7 @@
     "\n",
     "        all_combinations.extend(new_combinations)\n",
     "\n",
-    "    print(f'  - created {len(all_combinations)} combinations')\n",
+    "    print(f\"  - created {len(all_combinations)} combinations\")\n",
     "    if len(all_combinations) > 5000000:\n",
     "        return lower_bound  # Abort if too large.\n",
     "\n",
@@ -725,7 +770,7 @@
     "    #     do not use that column.\n",
     "    # 2/ Merge all task with exactly same demands into one.\n",
     "    model = cp_model.CpModel()\n",
-    "    model.SetName(f'lower_bound_{problem.name}')\n",
+    "    model.SetName(f\"lower_bound_{problem.name}\")\n",
     "\n",
     "    vars_per_task = collections.defaultdict(list)\n",
     "    all_vars = []\n",
@@ -733,7 +778,7 @@
     "        min_duration = max_duration\n",
     "        for t in c:\n",
     "            min_duration = min(min_duration, duration_map[t])\n",
-    "        count = model.NewIntVar(0, min_duration, f'count_{t}')\n",
+    "        count = model.NewIntVar(0, min_duration, f\"count_{c}\")\n",
     "        all_vars.append(count)\n",
     "        for t in c:\n",
     "            vars_per_task[t].append(count)\n",
@@ -743,8 +788,7 @@
     "        model.Add(sum(vars_per_task[t]) >= duration_map[t])\n",
     "\n",
     "    # Objective\n",
-    "    objective_var = model.NewIntVar(lower_bound, sum_of_demands,\n",
-    "                                    'objective_var')\n",
+    "    objective_var = model.NewIntVar(lower_bound, sum_of_demands, \"objective_var\")\n",
     "    model.Add(objective_var == sum(all_vars))\n",
     "\n",
     "    model.Minimize(objective_var)\n",
@@ -755,24 +799,24 @@
     "    solver.parameters.max_time_in_seconds = _PREEMPTIVE_LB_TIME_LIMIT.value\n",
     "    status = solver.Solve(model)\n",
     "    if status == cp_model.OPTIMAL or status == cp_model.FEASIBLE:\n",
-    "        status_str = 'optimal' if status == cp_model.OPTIMAL else ''\n",
+    "        status_str = \"optimal\" if status == cp_model.OPTIMAL else \"\"\n",
     "        lower_bound = max(lower_bound, int(solver.BestObjectiveBound()))\n",
-    "        print(f'  - {status_str} static lower bound = {lower_bound}',\n",
-    "              flush=True)\n",
+    "        print(f\"  - {status_str} static lower bound = {lower_bound}\", flush=True)\n",
     "\n",
     "    return lower_bound\n",
     "\n",
     "\n",
     "def main(_):\n",
-    "    rcpsp_parser = pywraprcpsp.RcpspParser()\n",
-    "    rcpsp_parser.ParseFile(_INPUT.value)\n",
+    "    rcpsp_parser = rcpsp.RcpspParser()\n",
+    "    rcpsp_parser.parse_file(_INPUT.value)\n",
     "\n",
-    "    problem = rcpsp_parser.Problem()\n",
+    "    problem = rcpsp_parser.problem()\n",
     "    PrintProblemStatistics(problem)\n",
     "\n",
     "    intervals_of_tasks, after = AnalyseDependencyGraph(problem)\n",
     "    delays, initial_solution, optimal_found = ComputeDelaysBetweenNodes(\n",
-    "        problem, intervals_of_tasks)\n",
+    "        problem, intervals_of_tasks\n",
+    "    )\n",
     "\n",
     "    last_task = len(problem.tasks) - 1\n",
     "    key = (0, last_task)\n",
@@ -780,17 +824,19 @@
     "    if not optimal_found and _PREEMPTIVE_LB_TIME_LIMIT.value > 0.0:\n",
     "        lower_bound = ComputePreemptiveLowerBound(problem, after, lower_bound)\n",
     "\n",
-    "    SolveRcpsp(problem=problem,\n",
-    "               proto_file=_OUTPUT_PROTO.value,\n",
-    "               params=_PARAMS.value,\n",
-    "               active_tasks=set(range(1, last_task)),\n",
-    "               source=0,\n",
-    "               sink=last_task,\n",
-    "               intervals_of_tasks=intervals_of_tasks,\n",
-    "               delays=delays,\n",
-    "               in_main_solve=True,\n",
-    "               initial_solution=initial_solution,\n",
-    "               lower_bound=lower_bound)\n",
+    "    SolveRcpsp(\n",
+    "        problem=problem,\n",
+    "        proto_file=_OUTPUT_PROTO.value,\n",
+    "        params=_PARAMS.value,\n",
+    "        active_tasks=set(range(1, last_task)),\n",
+    "        source=0,\n",
+    "        sink=last_task,\n",
+    "        intervals_of_tasks=intervals_of_tasks,\n",
+    "        delays=delays,\n",
+    "        in_main_solve=True,\n",
+    "        initial_solution=initial_solution,\n",
+    "        lower_bound=lower_bound,\n",
+    "    )\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/examples/shift_scheduling_sat.ipynb b/examples/notebook/examples/shift_scheduling_sat.ipynb
index 95ae9d0968..0d92d87663 100644
--- a/examples/notebook/examples/shift_scheduling_sat.ipynb
+++ b/examples/notebook/examples/shift_scheduling_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Creates a shift scheduling problem and solves it.\n"
    ]
   },
@@ -86,28 +87,30 @@
     "from google.protobuf import text_format\n",
     "\n",
     "_OUTPUT_PROTO = flags.DEFINE_string(\n",
-    "    'output_proto', '', 'Output file to write the cp_model proto to.')\n",
-    "_PARAMS = flags.DEFINE_string('params', 'max_time_in_seconds:10.0',\n",
-    "                              'Sat solver parameters.')\n",
+    "    \"output_proto\", \"\", \"Output file to write the cp_model proto to.\"\n",
+    ")\n",
+    "_PARAMS = flags.DEFINE_string(\n",
+    "    \"params\", \"max_time_in_seconds:10.0\", \"Sat solver parameters.\"\n",
+    ")\n",
     "\n",
     "\n",
     "def negated_bounded_span(works, start, length):\n",
     "    \"\"\"Filters an isolated sub-sequence of variables assined to True.\n",
     "\n",
-    "  Extract the span of Boolean variables [start, start + length), negate them,\n",
-    "  and if there is variables to the left/right of this span, surround the span by\n",
-    "  them in non negated form.\n",
+    "    Extract the span of Boolean variables [start, start + length), negate them,\n",
+    "    and if there is variables to the left/right of this span, surround the span by\n",
+    "    them in non negated form.\n",
     "\n",
-    "  Args:\n",
-    "    works: a list of variables to extract the span from.\n",
-    "    start: the start to the span.\n",
-    "    length: the length of the span.\n",
+    "    Args:\n",
+    "      works: a list of variables to extract the span from.\n",
+    "      start: the start to the span.\n",
+    "      length: the length of the span.\n",
     "\n",
-    "  Returns:\n",
-    "    a list of variables which conjunction will be false if the sub-list is\n",
-    "    assigned to True, and correctly bounded by variables assigned to False,\n",
-    "    or by the start or end of works.\n",
-    "  \"\"\"\n",
+    "    Returns:\n",
+    "      a list of variables which conjunction will be false if the sub-list is\n",
+    "      assigned to True, and correctly bounded by variables assigned to False,\n",
+    "      or by the start or end of works.\n",
+    "    \"\"\"\n",
     "    sequence = []\n",
     "    # Left border (start of works, or works[start - 1])\n",
     "    if start > 0:\n",
@@ -120,35 +123,36 @@
     "    return sequence\n",
     "\n",
     "\n",
-    "def add_soft_sequence_constraint(model, works, hard_min, soft_min, min_cost,\n",
-    "                                 soft_max, hard_max, max_cost, prefix):\n",
+    "def add_soft_sequence_constraint(\n",
+    "    model, works, hard_min, soft_min, min_cost, soft_max, hard_max, max_cost, prefix\n",
+    "):\n",
     "    \"\"\"Sequence constraint on true variables with soft and hard bounds.\n",
     "\n",
-    "  This constraint look at every maximal contiguous sequence of variables\n",
-    "  assigned to true. If forbids sequence of length < hard_min or > hard_max.\n",
-    "  Then it creates penalty terms if the length is < soft_min or > soft_max.\n",
+    "    This constraint look at every maximal contiguous sequence of variables\n",
+    "    assigned to true. If forbids sequence of length < hard_min or > hard_max.\n",
+    "    Then it creates penalty terms if the length is < soft_min or > soft_max.\n",
     "\n",
-    "  Args:\n",
-    "    model: the sequence constraint is built on this model.\n",
-    "    works: a list of Boolean variables.\n",
-    "    hard_min: any sequence of true variables must have a length of at least\n",
-    "      hard_min.\n",
-    "    soft_min: any sequence should have a length of at least soft_min, or a\n",
-    "      linear penalty on the delta will be added to the objective.\n",
-    "    min_cost: the coefficient of the linear penalty if the length is less than\n",
-    "      soft_min.\n",
-    "    soft_max: any sequence should have a length of at most soft_max, or a linear\n",
-    "      penalty on the delta will be added to the objective.\n",
-    "    hard_max: any sequence of true variables must have a length of at most\n",
-    "      hard_max.\n",
-    "    max_cost: the coefficient of the linear penalty if the length is more than\n",
-    "      soft_max.\n",
-    "    prefix: a base name for penalty literals.\n",
+    "    Args:\n",
+    "      model: the sequence constraint is built on this model.\n",
+    "      works: a list of Boolean variables.\n",
+    "      hard_min: any sequence of true variables must have a length of at least\n",
+    "        hard_min.\n",
+    "      soft_min: any sequence should have a length of at least soft_min, or a\n",
+    "        linear penalty on the delta will be added to the objective.\n",
+    "      min_cost: the coefficient of the linear penalty if the length is less than\n",
+    "        soft_min.\n",
+    "      soft_max: any sequence should have a length of at most soft_max, or a linear\n",
+    "        penalty on the delta will be added to the objective.\n",
+    "      hard_max: any sequence of true variables must have a length of at most\n",
+    "        hard_max.\n",
+    "      max_cost: the coefficient of the linear penalty if the length is more than\n",
+    "        soft_max.\n",
+    "      prefix: a base name for penalty literals.\n",
     "\n",
-    "  Returns:\n",
-    "    a tuple (variables_list, coefficient_list) containing the different\n",
-    "    penalties created by the sequence constraint.\n",
-    "  \"\"\"\n",
+    "    Returns:\n",
+    "      a tuple (variables_list, coefficient_list) containing the different\n",
+    "      penalties created by the sequence constraint.\n",
+    "    \"\"\"\n",
     "    cost_literals = []\n",
     "    cost_coefficients = []\n",
     "\n",
@@ -162,7 +166,7 @@
     "        for length in range(hard_min, soft_min):\n",
     "            for start in range(len(works) - length + 1):\n",
     "                span = negated_bounded_span(works, start, length)\n",
-    "                name = ': under_span(start=%i, length=%i)' % (start, length)\n",
+    "                name = \": under_span(start=%i, length=%i)\" % (start, length)\n",
     "                lit = model.NewBoolVar(prefix + name)\n",
     "                span.append(lit)\n",
     "                model.AddBoolOr(span)\n",
@@ -176,7 +180,7 @@
     "        for length in range(soft_max + 1, hard_max + 1):\n",
     "            for start in range(len(works) - length + 1):\n",
     "                span = negated_bounded_span(works, start, length)\n",
-    "                name = ': over_span(start=%i, length=%i)' % (start, length)\n",
+    "                name = \": over_span(start=%i, length=%i)\" % (start, length)\n",
     "                lit = model.NewBoolVar(prefix + name)\n",
     "                span.append(lit)\n",
     "                model.AddBoolOr(span)\n",
@@ -186,61 +190,61 @@
     "\n",
     "    # Just forbid any sequence of true variables with length hard_max + 1\n",
     "    for start in range(len(works) - hard_max):\n",
-    "        model.AddBoolOr(\n",
-    "            [works[i].Not() for i in range(start, start + hard_max + 1)])\n",
+    "        model.AddBoolOr([works[i].Not() for i in range(start, start + hard_max + 1)])\n",
     "    return cost_literals, cost_coefficients\n",
     "\n",
     "\n",
-    "def add_soft_sum_constraint(model, works, hard_min, soft_min, min_cost,\n",
-    "                            soft_max, hard_max, max_cost, prefix):\n",
+    "def add_soft_sum_constraint(\n",
+    "    model, works, hard_min, soft_min, min_cost, soft_max, hard_max, max_cost, prefix\n",
+    "):\n",
     "    \"\"\"Sum constraint with soft and hard bounds.\n",
     "\n",
-    "  This constraint counts the variables assigned to true from works.\n",
-    "  If forbids sum < hard_min or > hard_max.\n",
-    "  Then it creates penalty terms if the sum is < soft_min or > soft_max.\n",
+    "    This constraint counts the variables assigned to true from works.\n",
+    "    If forbids sum < hard_min or > hard_max.\n",
+    "    Then it creates penalty terms if the sum is < soft_min or > soft_max.\n",
     "\n",
-    "  Args:\n",
-    "    model: the sequence constraint is built on this model.\n",
-    "    works: a list of Boolean variables.\n",
-    "    hard_min: any sequence of true variables must have a sum of at least\n",
-    "      hard_min.\n",
-    "    soft_min: any sequence should have a sum of at least soft_min, or a linear\n",
-    "      penalty on the delta will be added to the objective.\n",
-    "    min_cost: the coefficient of the linear penalty if the sum is less than\n",
-    "      soft_min.\n",
-    "    soft_max: any sequence should have a sum of at most soft_max, or a linear\n",
-    "      penalty on the delta will be added to the objective.\n",
-    "    hard_max: any sequence of true variables must have a sum of at most\n",
-    "      hard_max.\n",
-    "    max_cost: the coefficient of the linear penalty if the sum is more than\n",
-    "      soft_max.\n",
-    "    prefix: a base name for penalty variables.\n",
+    "    Args:\n",
+    "      model: the sequence constraint is built on this model.\n",
+    "      works: a list of Boolean variables.\n",
+    "      hard_min: any sequence of true variables must have a sum of at least\n",
+    "        hard_min.\n",
+    "      soft_min: any sequence should have a sum of at least soft_min, or a linear\n",
+    "        penalty on the delta will be added to the objective.\n",
+    "      min_cost: the coefficient of the linear penalty if the sum is less than\n",
+    "        soft_min.\n",
+    "      soft_max: any sequence should have a sum of at most soft_max, or a linear\n",
+    "        penalty on the delta will be added to the objective.\n",
+    "      hard_max: any sequence of true variables must have a sum of at most\n",
+    "        hard_max.\n",
+    "      max_cost: the coefficient of the linear penalty if the sum is more than\n",
+    "        soft_max.\n",
+    "      prefix: a base name for penalty variables.\n",
     "\n",
-    "  Returns:\n",
-    "    a tuple (variables_list, coefficient_list) containing the different\n",
-    "    penalties created by the sequence constraint.\n",
-    "  \"\"\"\n",
+    "    Returns:\n",
+    "      a tuple (variables_list, coefficient_list) containing the different\n",
+    "      penalties created by the sequence constraint.\n",
+    "    \"\"\"\n",
     "    cost_variables = []\n",
     "    cost_coefficients = []\n",
-    "    sum_var = model.NewIntVar(hard_min, hard_max, '')\n",
+    "    sum_var = model.NewIntVar(hard_min, hard_max, \"\")\n",
     "    # This adds the hard constraints on the sum.\n",
     "    model.Add(sum_var == sum(works))\n",
     "\n",
     "    # Penalize sums below the soft_min target.\n",
     "    if soft_min > hard_min and min_cost > 0:\n",
-    "        delta = model.NewIntVar(-len(works), len(works), '')\n",
+    "        delta = model.NewIntVar(-len(works), len(works), \"\")\n",
     "        model.Add(delta == soft_min - sum_var)\n",
     "        # TODO(user): Compare efficiency with only excess >= soft_min - sum_var.\n",
-    "        excess = model.NewIntVar(0, 7, prefix + ': under_sum')\n",
+    "        excess = model.NewIntVar(0, 7, prefix + \": under_sum\")\n",
     "        model.AddMaxEquality(excess, [delta, 0])\n",
     "        cost_variables.append(excess)\n",
     "        cost_coefficients.append(min_cost)\n",
     "\n",
     "    # Penalize sums above the soft_max target.\n",
     "    if soft_max < hard_max and max_cost > 0:\n",
-    "        delta = model.NewIntVar(-7, 7, '')\n",
+    "        delta = model.NewIntVar(-7, 7, \"\")\n",
     "        model.Add(delta == sum_var - soft_max)\n",
-    "        excess = model.NewIntVar(0, 7, prefix + ': over_sum')\n",
+    "        excess = model.NewIntVar(0, 7, prefix + \": over_sum\")\n",
     "        model.AddMaxEquality(excess, [delta, 0])\n",
     "        cost_variables.append(excess)\n",
     "        cost_coefficients.append(max_cost)\n",
@@ -253,7 +257,7 @@
     "    # Data\n",
     "    num_employees = 8\n",
     "    num_weeks = 3\n",
-    "    shifts = ['O', 'M', 'A', 'N']\n",
+    "    shifts = [\"O\", \"M\", \"A\", \"N\"]\n",
     "\n",
     "    # Fixed assignment: (employee, shift, day).\n",
     "    # This fixes the first 2 days of the schedule.\n",
@@ -286,7 +290,7 @@
     "        (5, 3, 10, -2),\n",
     "        # Employee 2 does not want a night shift on the first Friday (positive\n",
     "        # weight).\n",
-    "        (2, 3, 4, 4)\n",
+    "        (2, 3, 4, 4),\n",
     "    ]\n",
     "\n",
     "    # Shift constraints on continuous sequence :\n",
@@ -343,7 +347,7 @@
     "    for e in range(num_employees):\n",
     "        for s in range(num_shifts):\n",
     "            for d in range(num_days):\n",
-    "                work[e, s, d] = model.NewBoolVar('work%i_%i_%i' % (e, s, d))\n",
+    "                work[e, s, d] = model.NewBoolVar(\"work%i_%i_%i\" % (e, s, d))\n",
     "\n",
     "    # Linear terms of the objective in a minimization context.\n",
     "    obj_int_vars = []\n",
@@ -371,9 +375,16 @@
     "        for e in range(num_employees):\n",
     "            works = [work[e, shift, d] for d in range(num_days)]\n",
     "            variables, coeffs = add_soft_sequence_constraint(\n",
-    "                model, works, hard_min, soft_min, min_cost, soft_max, hard_max,\n",
+    "                model,\n",
+    "                works,\n",
+    "                hard_min,\n",
+    "                soft_min,\n",
+    "                min_cost,\n",
+    "                soft_max,\n",
+    "                hard_max,\n",
     "                max_cost,\n",
-    "                'shift_constraint(employee %i, shift %i)' % (e, shift))\n",
+    "                \"shift_constraint(employee %i, shift %i)\" % (e, shift),\n",
+    "            )\n",
     "            obj_bool_vars.extend(variables)\n",
     "            obj_bool_coeffs.extend(coeffs)\n",
     "\n",
@@ -384,10 +395,17 @@
     "            for w in range(num_weeks):\n",
     "                works = [work[e, shift, d + w * 7] for d in range(7)]\n",
     "                variables, coeffs = add_soft_sum_constraint(\n",
-    "                    model, works, hard_min, soft_min, min_cost, soft_max,\n",
-    "                    hard_max, max_cost,\n",
-    "                    'weekly_sum_constraint(employee %i, shift %i, week %i)' %\n",
-    "                    (e, shift, w))\n",
+    "                    model,\n",
+    "                    works,\n",
+    "                    hard_min,\n",
+    "                    soft_min,\n",
+    "                    min_cost,\n",
+    "                    soft_max,\n",
+    "                    hard_max,\n",
+    "                    max_cost,\n",
+    "                    \"weekly_sum_constraint(employee %i, shift %i, week %i)\"\n",
+    "                    % (e, shift, w),\n",
+    "                )\n",
     "                obj_int_vars.extend(variables)\n",
     "                obj_int_coeffs.extend(coeffs)\n",
     "\n",
@@ -396,14 +414,15 @@
     "        for e in range(num_employees):\n",
     "            for d in range(num_days - 1):\n",
     "                transition = [\n",
-    "                    work[e, previous_shift, d].Not(), work[e, next_shift,\n",
-    "                                                           d + 1].Not()\n",
+    "                    work[e, previous_shift, d].Not(),\n",
+    "                    work[e, next_shift, d + 1].Not(),\n",
     "                ]\n",
     "                if cost == 0:\n",
     "                    model.AddBoolOr(transition)\n",
     "                else:\n",
     "                    trans_var = model.NewBoolVar(\n",
-    "                        'transition (employee=%i, day=%i)' % (e, d))\n",
+    "                        \"transition (employee=%i, day=%i)\" % (e, d)\n",
+    "                    )\n",
     "                    transition.append(trans_var)\n",
     "                    model.AddBoolOr(transition)\n",
     "                    obj_bool_vars.append(trans_var)\n",
@@ -416,28 +435,25 @@
     "                works = [work[e, s, w * 7 + d] for e in range(num_employees)]\n",
     "                # Ignore Off shift.\n",
     "                min_demand = weekly_cover_demands[d][s - 1]\n",
-    "                worked = model.NewIntVar(min_demand, num_employees, '')\n",
+    "                worked = model.NewIntVar(min_demand, num_employees, \"\")\n",
     "                model.Add(worked == sum(works))\n",
     "                over_penalty = excess_cover_penalties[s - 1]\n",
     "                if over_penalty > 0:\n",
-    "                    name = 'excess_demand(shift=%i, week=%i, day=%i)' % (s, w,\n",
-    "                                                                         d)\n",
-    "                    excess = model.NewIntVar(0, num_employees - min_demand,\n",
-    "                                             name)\n",
+    "                    name = \"excess_demand(shift=%i, week=%i, day=%i)\" % (s, w, d)\n",
+    "                    excess = model.NewIntVar(0, num_employees - min_demand, name)\n",
     "                    model.Add(excess == worked - min_demand)\n",
     "                    obj_int_vars.append(excess)\n",
     "                    obj_int_coeffs.append(over_penalty)\n",
     "\n",
     "    # Objective\n",
     "    model.Minimize(\n",
-    "        sum(obj_bool_vars[i] * obj_bool_coeffs[i]\n",
-    "            for i in range(len(obj_bool_vars))) +\n",
-    "        sum(obj_int_vars[i] * obj_int_coeffs[i]\n",
-    "            for i in range(len(obj_int_vars))))\n",
+    "        sum(obj_bool_vars[i] * obj_bool_coeffs[i] for i in range(len(obj_bool_vars)))\n",
+    "        + sum(obj_int_vars[i] * obj_int_coeffs[i] for i in range(len(obj_int_vars)))\n",
+    "    )\n",
     "\n",
     "    if output_proto:\n",
-    "        print('Writing proto to %s' % output_proto)\n",
-    "        with open(output_proto, 'w') as text_file:\n",
+    "        print(\"Writing proto to %s\" % output_proto)\n",
+    "        with open(output_proto, \"w\") as text_file:\n",
     "            text_file.write(str(model))\n",
     "\n",
     "    # Solve the model.\n",
@@ -450,38 +466,40 @@
     "    # Print solution.\n",
     "    if status == cp_model.OPTIMAL or status == cp_model.FEASIBLE:\n",
     "        print()\n",
-    "        header = '          '\n",
+    "        header = \"          \"\n",
     "        for w in range(num_weeks):\n",
-    "            header += 'M T W T F S S '\n",
+    "            header += \"M T W T F S S \"\n",
     "        print(header)\n",
     "        for e in range(num_employees):\n",
-    "            schedule = ''\n",
+    "            schedule = \"\"\n",
     "            for d in range(num_days):\n",
     "                for s in range(num_shifts):\n",
     "                    if solver.BooleanValue(work[e, s, d]):\n",
-    "                        schedule += shifts[s] + ' '\n",
-    "            print('worker %i: %s' % (e, schedule))\n",
+    "                        schedule += shifts[s] + \" \"\n",
+    "            print(\"worker %i: %s\" % (e, schedule))\n",
     "        print()\n",
-    "        print('Penalties:')\n",
+    "        print(\"Penalties:\")\n",
     "        for i, var in enumerate(obj_bool_vars):\n",
     "            if solver.BooleanValue(var):\n",
     "                penalty = obj_bool_coeffs[i]\n",
     "                if penalty > 0:\n",
-    "                    print('  %s violated, penalty=%i' % (var.Name(), penalty))\n",
+    "                    print(\"  %s violated, penalty=%i\" % (var.Name(), penalty))\n",
     "                else:\n",
-    "                    print('  %s fulfilled, gain=%i' % (var.Name(), -penalty))\n",
+    "                    print(\"  %s fulfilled, gain=%i\" % (var.Name(), -penalty))\n",
     "\n",
     "        for i, var in enumerate(obj_int_vars):\n",
     "            if solver.Value(var) > 0:\n",
-    "                print('  %s violated by %i, linear penalty=%i' %\n",
-    "                      (var.Name(), solver.Value(var), obj_int_coeffs[i]))\n",
+    "                print(\n",
+    "                    \"  %s violated by %i, linear penalty=%i\"\n",
+    "                    % (var.Name(), solver.Value(var), obj_int_coeffs[i])\n",
+    "                )\n",
     "\n",
     "    print()\n",
-    "    print('Statistics')\n",
-    "    print('  - status          : %s' % solver.StatusName(status))\n",
-    "    print('  - conflicts       : %i' % solver.NumConflicts())\n",
-    "    print('  - branches        : %i' % solver.NumBranches())\n",
-    "    print('  - wall time       : %f s' % solver.WallTime())\n",
+    "    print(\"Statistics\")\n",
+    "    print(\"  - status          : %s\" % solver.StatusName(status))\n",
+    "    print(\"  - conflicts       : %i\" % solver.NumConflicts())\n",
+    "    print(\"  - branches        : %i\" % solver.NumBranches())\n",
+    "    print(\"  - wall time       : %f s\" % solver.WallTime())\n",
     "\n",
     "\n",
     "def main(_):\n",
diff --git a/examples/notebook/examples/single_machine_scheduling_with_setup_release_due_dates_sat.ipynb b/examples/notebook/examples/single_machine_scheduling_with_setup_release_due_dates_sat.ipynb
index ffb383ab65..0111f15bb8 100644
--- a/examples/notebook/examples/single_machine_scheduling_with_setup_release_due_dates_sat.ipynb
+++ b/examples/notebook/examples/single_machine_scheduling_with_setup_release_due_dates_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Single machine jobshop with setup times, release dates and due dates.\n"
    ]
   },
@@ -86,19 +87,22 @@
     "from google.protobuf import text_format\n",
     "from ortools.sat.python import cp_model\n",
     "\n",
-    "#----------------------------------------------------------------------------\n",
+    "# ----------------------------------------------------------------------------\n",
     "# Command line arguments.\n",
     "_OUTPUT_PROTO = flags.DEFINE_string(\n",
-    "    'output_proto', '', 'Output file to write the cp_model proto to.')\n",
+    "    \"output_proto\", \"\", \"Output file to write the cp_model proto to.\"\n",
+    ")\n",
     "_PARAMS = flags.DEFINE_string(\n",
-    "    'params',\n",
-    "    'num_search_workers:16,log_search_progress:true,max_time_in_seconds:45',\n",
-    "    'Sat solver parameters.')\n",
-    "_PREPROCESS = flags.DEFINE_bool('--preprocess_times', True,\n",
-    "                                'Preprocess setup times and durations')\n",
+    "    \"params\",\n",
+    "    \"num_search_workers:16,log_search_progress:true,max_time_in_seconds:45\",\n",
+    "    \"Sat solver parameters.\",\n",
+    ")\n",
+    "_PREPROCESS = flags.DEFINE_bool(\n",
+    "    \"--preprocess_times\", True, \"Preprocess setup times and durations\"\n",
+    ")\n",
     "\n",
     "\n",
-    "#----------------------------------------------------------------------------\n",
+    "# ----------------------------------------------------------------------------\n",
     "# Intermediate solution printer\n",
     "class SolutionPrinter(cp_model.CpSolverSolutionCallback):\n",
     "    \"\"\"Print intermediate solutions.\"\"\"\n",
@@ -109,8 +113,10 @@
     "\n",
     "    def on_solution_callback(self):\n",
     "        \"\"\"Called after each new solution found.\"\"\"\n",
-    "        print('Solution %i, time = %f s, objective = %i' %\n",
-    "              (self.__solution_count, self.WallTime(), self.ObjectiveValue()))\n",
+    "        print(\n",
+    "            \"Solution %i, time = %f s, objective = %i\"\n",
+    "            % (self.__solution_count, self.WallTime(), self.ObjectiveValue())\n",
+    "        )\n",
     "        self.__solution_count += 1\n",
     "\n",
     "\n",
@@ -120,110 +126,343 @@
     "    parameters = _PARAMS.value\n",
     "    output_proto_file = _OUTPUT_PROTO.value\n",
     "\n",
-    "    #----------------------------------------------------------------------------\n",
+    "    # ----------------------------------------------------------------------------\n",
     "    # Data.\n",
     "\n",
     "    job_durations = [\n",
-    "        2546, 8589, 5953, 3710, 3630, 3016, 4148, 8706, 1604, 5502, 9983, 6209,\n",
-    "        9920, 7860, 2176\n",
+    "        2546,\n",
+    "        8589,\n",
+    "        5953,\n",
+    "        3710,\n",
+    "        3630,\n",
+    "        3016,\n",
+    "        4148,\n",
+    "        8706,\n",
+    "        1604,\n",
+    "        5502,\n",
+    "        9983,\n",
+    "        6209,\n",
+    "        9920,\n",
+    "        7860,\n",
+    "        2176,\n",
     "    ]\n",
     "\n",
     "    setup_times = [\n",
     "        [\n",
-    "            3559, 1638, 2000, 3676, 2741, 2439, 2406, 1526, 1600, 3356, 4324,\n",
-    "            1923, 3663, 4103, 2215\n",
+    "            3559,\n",
+    "            1638,\n",
+    "            2000,\n",
+    "            3676,\n",
+    "            2741,\n",
+    "            2439,\n",
+    "            2406,\n",
+    "            1526,\n",
+    "            1600,\n",
+    "            3356,\n",
+    "            4324,\n",
+    "            1923,\n",
+    "            3663,\n",
+    "            4103,\n",
+    "            2215,\n",
     "        ],\n",
     "        [\n",
-    "            1442, 3010, 1641, 4490, 2060, 2143, 3376, 3891, 3513, 2855, 2653,\n",
-    "            1471, 2257, 1186, 2354\n",
+    "            1442,\n",
+    "            3010,\n",
+    "            1641,\n",
+    "            4490,\n",
+    "            2060,\n",
+    "            2143,\n",
+    "            3376,\n",
+    "            3891,\n",
+    "            3513,\n",
+    "            2855,\n",
+    "            2653,\n",
+    "            1471,\n",
+    "            2257,\n",
+    "            1186,\n",
+    "            2354,\n",
     "        ],\n",
     "        [\n",
-    "            1728, 3583, 3243, 4080, 2191, 3644, 4023, 3510, 2135, 1346, 1410,\n",
-    "            3565, 3181, 1126, 4169\n",
+    "            1728,\n",
+    "            3583,\n",
+    "            3243,\n",
+    "            4080,\n",
+    "            2191,\n",
+    "            3644,\n",
+    "            4023,\n",
+    "            3510,\n",
+    "            2135,\n",
+    "            1346,\n",
+    "            1410,\n",
+    "            3565,\n",
+    "            3181,\n",
+    "            1126,\n",
+    "            4169,\n",
     "        ],\n",
     "        [\n",
-    "            1291, 1703, 3103, 4001, 1712, 1137, 3341, 3485, 2557, 2435, 1972,\n",
-    "            1986, 1522, 4734, 2520\n",
+    "            1291,\n",
+    "            1703,\n",
+    "            3103,\n",
+    "            4001,\n",
+    "            1712,\n",
+    "            1137,\n",
+    "            3341,\n",
+    "            3485,\n",
+    "            2557,\n",
+    "            2435,\n",
+    "            1972,\n",
+    "            1986,\n",
+    "            1522,\n",
+    "            4734,\n",
+    "            2520,\n",
     "        ],\n",
     "        [\n",
-    "            4134, 2200, 1502, 3995, 1277, 1808, 1020, 2078, 2999, 1605, 1697,\n",
-    "            2323, 2268, 2288, 4856\n",
+    "            4134,\n",
+    "            2200,\n",
+    "            1502,\n",
+    "            3995,\n",
+    "            1277,\n",
+    "            1808,\n",
+    "            1020,\n",
+    "            2078,\n",
+    "            2999,\n",
+    "            1605,\n",
+    "            1697,\n",
+    "            2323,\n",
+    "            2268,\n",
+    "            2288,\n",
+    "            4856,\n",
     "        ],\n",
     "        [\n",
-    "            4974, 2480, 2492, 4088, 2587, 4652, 1478, 3942, 1222, 3305, 1206,\n",
-    "            1024, 2605, 3080, 3516\n",
+    "            4974,\n",
+    "            2480,\n",
+    "            2492,\n",
+    "            4088,\n",
+    "            2587,\n",
+    "            4652,\n",
+    "            1478,\n",
+    "            3942,\n",
+    "            1222,\n",
+    "            3305,\n",
+    "            1206,\n",
+    "            1024,\n",
+    "            2605,\n",
+    "            3080,\n",
+    "            3516,\n",
     "        ],\n",
     "        [\n",
-    "            1903, 2584, 2104, 1609, 4745, 2691, 1539, 2544, 2499, 2074, 4793,\n",
-    "            1756, 2190, 1298, 2605\n",
+    "            1903,\n",
+    "            2584,\n",
+    "            2104,\n",
+    "            1609,\n",
+    "            4745,\n",
+    "            2691,\n",
+    "            1539,\n",
+    "            2544,\n",
+    "            2499,\n",
+    "            2074,\n",
+    "            4793,\n",
+    "            1756,\n",
+    "            2190,\n",
+    "            1298,\n",
+    "            2605,\n",
     "        ],\n",
     "        [\n",
-    "            1407, 2536, 2296, 1769, 1449, 3386, 3046, 1180, 4132, 4783, 3386,\n",
-    "            3429, 2450, 3376, 3719\n",
+    "            1407,\n",
+    "            2536,\n",
+    "            2296,\n",
+    "            1769,\n",
+    "            1449,\n",
+    "            3386,\n",
+    "            3046,\n",
+    "            1180,\n",
+    "            4132,\n",
+    "            4783,\n",
+    "            3386,\n",
+    "            3429,\n",
+    "            2450,\n",
+    "            3376,\n",
+    "            3719,\n",
     "        ],\n",
     "        [\n",
-    "            3026, 1637, 3628, 3096, 1498, 4947, 1912, 3703, 4107, 4730, 1805,\n",
-    "            2189, 1789, 1985, 3586\n",
+    "            3026,\n",
+    "            1637,\n",
+    "            3628,\n",
+    "            3096,\n",
+    "            1498,\n",
+    "            4947,\n",
+    "            1912,\n",
+    "            3703,\n",
+    "            4107,\n",
+    "            4730,\n",
+    "            1805,\n",
+    "            2189,\n",
+    "            1789,\n",
+    "            1985,\n",
+    "            3586,\n",
     "        ],\n",
     "        [\n",
-    "            3940, 1342, 1601, 2737, 1748, 3771, 4052, 1619, 2558, 3782, 4383,\n",
-    "            3451, 4904, 1108, 1750\n",
+    "            3940,\n",
+    "            1342,\n",
+    "            1601,\n",
+    "            2737,\n",
+    "            1748,\n",
+    "            3771,\n",
+    "            4052,\n",
+    "            1619,\n",
+    "            2558,\n",
+    "            3782,\n",
+    "            4383,\n",
+    "            3451,\n",
+    "            4904,\n",
+    "            1108,\n",
+    "            1750,\n",
     "        ],\n",
     "        [\n",
-    "            1348, 3162, 1507, 3936, 1453, 2953, 4182, 2968, 3134, 1042, 3175,\n",
-    "            2805, 4901, 1735, 1654\n",
+    "            1348,\n",
+    "            3162,\n",
+    "            1507,\n",
+    "            3936,\n",
+    "            1453,\n",
+    "            2953,\n",
+    "            4182,\n",
+    "            2968,\n",
+    "            3134,\n",
+    "            1042,\n",
+    "            3175,\n",
+    "            2805,\n",
+    "            4901,\n",
+    "            1735,\n",
+    "            1654,\n",
     "        ],\n",
     "        [\n",
-    "            1099, 1711, 1245, 1067, 4343, 3407, 1108, 1784, 4803, 2342, 3377,\n",
-    "            2037, 3563, 1621, 2840\n",
+    "            1099,\n",
+    "            1711,\n",
+    "            1245,\n",
+    "            1067,\n",
+    "            4343,\n",
+    "            3407,\n",
+    "            1108,\n",
+    "            1784,\n",
+    "            4803,\n",
+    "            2342,\n",
+    "            3377,\n",
+    "            2037,\n",
+    "            3563,\n",
+    "            1621,\n",
+    "            2840,\n",
     "        ],\n",
     "        [\n",
-    "            2573, 4222, 3164, 2563, 3231, 4731, 2395, 1033, 4795, 3288, 2335,\n",
-    "            4935, 4066, 1440, 4979\n",
+    "            2573,\n",
+    "            4222,\n",
+    "            3164,\n",
+    "            2563,\n",
+    "            3231,\n",
+    "            4731,\n",
+    "            2395,\n",
+    "            1033,\n",
+    "            4795,\n",
+    "            3288,\n",
+    "            2335,\n",
+    "            4935,\n",
+    "            4066,\n",
+    "            1440,\n",
+    "            4979,\n",
     "        ],\n",
     "        [\n",
-    "            3321, 1666, 3573, 2377, 4649, 4600, 1065, 2475, 3658, 3374, 1138,\n",
-    "            4367, 4728, 3032, 2198\n",
+    "            3321,\n",
+    "            1666,\n",
+    "            3573,\n",
+    "            2377,\n",
+    "            4649,\n",
+    "            4600,\n",
+    "            1065,\n",
+    "            2475,\n",
+    "            3658,\n",
+    "            3374,\n",
+    "            1138,\n",
+    "            4367,\n",
+    "            4728,\n",
+    "            3032,\n",
+    "            2198,\n",
     "        ],\n",
     "        [\n",
-    "            2986, 1180, 4095, 3132, 3987, 3880, 3526, 1460, 4885, 3827, 4945,\n",
-    "            4419, 3486, 3805, 3804\n",
+    "            2986,\n",
+    "            1180,\n",
+    "            4095,\n",
+    "            3132,\n",
+    "            3987,\n",
+    "            3880,\n",
+    "            3526,\n",
+    "            1460,\n",
+    "            4885,\n",
+    "            3827,\n",
+    "            4945,\n",
+    "            4419,\n",
+    "            3486,\n",
+    "            3805,\n",
+    "            3804,\n",
     "        ],\n",
     "        [\n",
-    "            4163, 3441, 1217, 2941, 1210, 3794, 1779, 1904, 4255, 4967, 4003,\n",
-    "            3873, 1002, 2055, 4295\n",
+    "            4163,\n",
+    "            3441,\n",
+    "            1217,\n",
+    "            2941,\n",
+    "            1210,\n",
+    "            3794,\n",
+    "            1779,\n",
+    "            1904,\n",
+    "            4255,\n",
+    "            4967,\n",
+    "            4003,\n",
+    "            3873,\n",
+    "            1002,\n",
+    "            2055,\n",
+    "            4295,\n",
     "        ],\n",
     "    ]\n",
     "\n",
     "    due_dates = [\n",
-    "        -1, -1, 28569, -1, 98104, 27644, 55274, 57364, -1, -1, 60875, 96637,\n",
-    "        77888, -1, -1\n",
-    "    ]\n",
-    "    release_dates = [\n",
-    "        0, 0, 0, 0, 19380, 0, 0, 48657, 0, 27932, 0, 0, 24876, 0, 0\n",
+    "        -1,\n",
+    "        -1,\n",
+    "        28569,\n",
+    "        -1,\n",
+    "        98104,\n",
+    "        27644,\n",
+    "        55274,\n",
+    "        57364,\n",
+    "        -1,\n",
+    "        -1,\n",
+    "        60875,\n",
+    "        96637,\n",
+    "        77888,\n",
+    "        -1,\n",
+    "        -1,\n",
     "    ]\n",
+    "    release_dates = [0, 0, 0, 0, 19380, 0, 0, 48657, 0, 27932, 0, 0, 24876, 0, 0]\n",
     "\n",
     "    precedences = [(0, 2), (1, 2)]\n",
     "\n",
-    "    #----------------------------------------------------------------------------\n",
+    "    # ----------------------------------------------------------------------------\n",
     "    # Helper data.\n",
     "    num_jobs = len(job_durations)\n",
     "    all_jobs = range(num_jobs)\n",
     "\n",
-    "    #----------------------------------------------------------------------------\n",
+    "    # ----------------------------------------------------------------------------\n",
     "    # Preprocess.\n",
     "    if _PREPROCESS.value:\n",
     "        for job_id in all_jobs:\n",
     "            min_incoming_setup = min(\n",
-    "                setup_times[j][job_id] for j in range(num_jobs + 1))\n",
+    "                setup_times[j][job_id] for j in range(num_jobs + 1)\n",
+    "            )\n",
     "            if release_dates[job_id] != 0:\n",
-    "                min_incoming_setup = min(min_incoming_setup,\n",
-    "                                         release_dates[job_id])\n",
+    "                min_incoming_setup = min(min_incoming_setup, release_dates[job_id])\n",
     "            if min_incoming_setup == 0:\n",
     "                continue\n",
     "\n",
-    "            print('job %i has a min incoming setup of %i' %\n",
-    "                  (job_id, min_incoming_setup))\n",
+    "            print(\n",
+    "                \"job %i has a min incoming setup of %i\" % (job_id, min_incoming_setup)\n",
+    "            )\n",
     "            # We can transfer some setup times to the duration of the job.\n",
     "            job_durations[job_id] += min_incoming_setup\n",
     "            # Decrease corresponding incoming setup times.\n",
@@ -233,36 +472,37 @@
     "            if release_dates[job_id] != 0:\n",
     "                release_dates[job_id] -= min_incoming_setup\n",
     "\n",
-    "    #----------------------------------------------------------------------------\n",
+    "    # ----------------------------------------------------------------------------\n",
     "    # Model.\n",
     "    model = cp_model.CpModel()\n",
     "\n",
-    "    #----------------------------------------------------------------------------\n",
+    "    # ----------------------------------------------------------------------------\n",
     "    # Compute a maximum makespan greedily.\n",
     "    horizon = sum(job_durations) + sum(\n",
-    "        max(setup_times[i][j]\n",
-    "            for i in range(num_jobs + 1))\n",
-    "        for j in range(num_jobs))\n",
-    "    print('Greedy horizon =', horizon)\n",
+    "        max(setup_times[i][j] for i in range(num_jobs + 1)) for j in range(num_jobs)\n",
+    "    )\n",
+    "    print(\"Greedy horizon =\", horizon)\n",
     "\n",
-    "    #----------------------------------------------------------------------------\n",
+    "    # ----------------------------------------------------------------------------\n",
     "    # Global storage of variables.\n",
     "    intervals = []\n",
     "    starts = []\n",
     "    ends = []\n",
     "\n",
-    "    #----------------------------------------------------------------------------\n",
+    "    # ----------------------------------------------------------------------------\n",
     "    # Scan the jobs and create the relevant variables and intervals.\n",
     "    for job_id in all_jobs:\n",
     "        duration = job_durations[job_id]\n",
     "        release_date = release_dates[job_id]\n",
     "        due_date = due_dates[job_id] if due_dates[job_id] != -1 else horizon\n",
-    "        print('job %2i: start = %5i, duration = %4i, end = %6i' %\n",
-    "              (job_id, release_date, duration, due_date))\n",
-    "        name_suffix = '_%i' % job_id\n",
-    "        start = model.NewIntVar(release_date, due_date, 's' + name_suffix)\n",
-    "        end = model.NewIntVar(release_date, due_date, 'e' + name_suffix)\n",
-    "        interval = model.NewIntervalVar(start, duration, end, 'i' + name_suffix)\n",
+    "        print(\n",
+    "            \"job %2i: start = %5i, duration = %4i, end = %6i\"\n",
+    "            % (job_id, release_date, duration, due_date)\n",
+    "        )\n",
+    "        name_suffix = \"_%i\" % job_id\n",
+    "        start = model.NewIntVar(release_date, due_date, \"s\" + name_suffix)\n",
+    "        end = model.NewIntVar(release_date, due_date, \"e\" + name_suffix)\n",
+    "        interval = model.NewIntervalVar(start, duration, end, \"i\" + name_suffix)\n",
     "        starts.append(start)\n",
     "        ends.append(end)\n",
     "        intervals.append(interval)\n",
@@ -270,59 +510,61 @@
     "    # No overlap constraint.\n",
     "    model.AddNoOverlap(intervals)\n",
     "\n",
-    "    #----------------------------------------------------------------------------\n",
+    "    # ----------------------------------------------------------------------------\n",
     "    # Transition times using a circuit constraint.\n",
     "    arcs = []\n",
     "    for i in all_jobs:\n",
     "        # Initial arc from the dummy node (0) to a task.\n",
-    "        start_lit = model.NewBoolVar('')\n",
-    "        arcs.append([0, i + 1, start_lit])\n",
+    "        start_lit = model.NewBoolVar(\"\")\n",
+    "        arcs.append((0, i + 1, start_lit))\n",
     "        # If this task is the first, set to minimum starting time.\n",
     "        min_start_time = max(release_dates[i], setup_times[0][i])\n",
     "        model.Add(starts[i] == min_start_time).OnlyEnforceIf(start_lit)\n",
     "        # Final arc from an arc to the dummy node.\n",
-    "        arcs.append([i + 1, 0, model.NewBoolVar('')])\n",
+    "        arcs.append((i + 1, 0, model.NewBoolVar(\"\")))\n",
     "\n",
     "        for j in all_jobs:\n",
     "            if i == j:\n",
     "                continue\n",
     "\n",
-    "            lit = model.NewBoolVar('%i follows %i' % (j, i))\n",
-    "            arcs.append([i + 1, j + 1, lit])\n",
+    "            lit = model.NewBoolVar(\"%i follows %i\" % (j, i))\n",
+    "            arcs.append((i + 1, j + 1, lit))\n",
     "\n",
     "            # We add the reified precedence to link the literal with the times of the\n",
     "            # two tasks.\n",
     "            # If release_dates[j] == 0, we can strenghten this precedence into an\n",
     "            # equality as we are minimizing the makespan.\n",
     "            if release_dates[j] == 0:\n",
-    "                model.Add(starts[j] == ends[i] +\n",
-    "                          setup_times[i + 1][j]).OnlyEnforceIf(lit)\n",
+    "                model.Add(starts[j] == ends[i] + setup_times[i + 1][j]).OnlyEnforceIf(\n",
+    "                    lit\n",
+    "                )\n",
     "            else:\n",
-    "                model.Add(starts[j] >= ends[i] +\n",
-    "                          setup_times[i + 1][j]).OnlyEnforceIf(lit)\n",
+    "                model.Add(starts[j] >= ends[i] + setup_times[i + 1][j]).OnlyEnforceIf(\n",
+    "                    lit\n",
+    "                )\n",
     "\n",
     "    model.AddCircuit(arcs)\n",
     "\n",
-    "    #----------------------------------------------------------------------------\n",
+    "    # ----------------------------------------------------------------------------\n",
     "    # Precedences.\n",
     "    for before, after in precedences:\n",
-    "        print('job %i is after job %i' % (after, before))\n",
+    "        print(\"job %i is after job %i\" % (after, before))\n",
     "        model.Add(ends[before] <= starts[after])\n",
     "\n",
-    "    #----------------------------------------------------------------------------\n",
+    "    # ----------------------------------------------------------------------------\n",
     "    # Objective.\n",
-    "    makespan = model.NewIntVar(0, horizon, 'makespan')\n",
+    "    makespan = model.NewIntVar(0, horizon, \"makespan\")\n",
     "    model.AddMaxEquality(makespan, ends)\n",
     "    model.Minimize(makespan)\n",
     "\n",
-    "    #----------------------------------------------------------------------------\n",
+    "    # ----------------------------------------------------------------------------\n",
     "    # Write problem to file.\n",
     "    if output_proto_file:\n",
-    "        print('Writing proto to %s' % output_proto_file)\n",
-    "        with open(output_proto_file, 'w') as text_file:\n",
+    "        print(\"Writing proto to %s\" % output_proto_file)\n",
+    "        with open(output_proto_file, \"w\") as text_file:\n",
     "            text_file.write(str(model))\n",
     "\n",
-    "    #----------------------------------------------------------------------------\n",
+    "    # ----------------------------------------------------------------------------\n",
     "    # Solve.\n",
     "    solver = cp_model.CpSolver()\n",
     "    if parameters:\n",
@@ -331,13 +573,14 @@
     "    solver.Solve(model, solution_printer)\n",
     "    for job_id in all_jobs:\n",
     "        print(\n",
-    "            'job %i starts at %i end ends at %i' %\n",
-    "            (job_id, solver.Value(starts[job_id]), solver.Value(ends[job_id])))\n",
+    "            \"job %i starts at %i end ends at %i\"\n",
+    "            % (job_id, solver.Value(starts[job_id]), solver.Value(ends[job_id]))\n",
+    "        )\n",
     "\n",
     "\n",
     "def main(argv: Sequence[str]) -> None:\n",
     "    if len(argv) > 1:\n",
-    "        raise app.UsageError('Too many command-line arguments.')\n",
+    "        raise app.UsageError(\"Too many command-line arguments.\")\n",
     "    single_machine_scheduling()\n",
     "\n",
     "\n",
diff --git a/examples/notebook/examples/spread_robots_sat.ipynb b/examples/notebook/examples/spread_robots_sat.ipynb
index 0d72c04dde..e205d0e682 100644
--- a/examples/notebook/examples/spread_robots_sat.ipynb
+++ b/examples/notebook/examples/spread_robots_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Maximize the minimum of pairwise distances between n robots in a square space.\n"
    ]
   },
@@ -87,14 +88,14 @@
     "from google.protobuf import text_format\n",
     "from ortools.sat.python import cp_model\n",
     "\n",
-    "_NUM_ROBOTS = flags.DEFINE_integer('num_robots', 8,\n",
-    "                                   'Number of robots to place.')\n",
-    "_ROOM_SIZE = flags.DEFINE_integer('room_size', 20,\n",
-    "                                  'Size of the square room where robots are.')\n",
+    "_NUM_ROBOTS = flags.DEFINE_integer(\"num_robots\", 8, \"Number of robots to place.\")\n",
+    "_ROOM_SIZE = flags.DEFINE_integer(\n",
+    "    \"room_size\", 20, \"Size of the square room where robots are.\"\n",
+    ")\n",
     "_PARAMS = flags.DEFINE_string(\n",
-    "    'params',\n",
-    "    'num_search_workers:16, max_time_in_seconds:20',\n",
-    "    'Sat solver parameters.',\n",
+    "    \"params\",\n",
+    "    \"num_search_workers:16, max_time_in_seconds:20\",\n",
+    "    \"Sat solver parameters.\",\n",
     ")\n",
     "\n",
     "\n",
@@ -103,8 +104,8 @@
     "    model = cp_model.CpModel()\n",
     "\n",
     "    # Create the list of coordinates (x, y) for each robot.\n",
-    "    x = [model.NewIntVar(1, room_size, f'x_{i}') for i in range(num_robots)]\n",
-    "    y = [model.NewIntVar(1, room_size, f'y_{i}') for i in range(num_robots)]\n",
+    "    x = [model.NewIntVar(1, room_size, f\"x_{i}\") for i in range(num_robots)]\n",
+    "    y = [model.NewIntVar(1, room_size, f\"y_{i}\") for i in range(num_robots)]\n",
     "\n",
     "    # The specification of the problem is to maximize the minimum euclidian\n",
     "    # distance between any two robots. Unfortunately, the euclidian distance\n",
@@ -123,30 +124,30 @@
     "    #   forall i:\n",
     "    #     scaled_min_square_distance <= scaling * (x_diff_sq[i] + y_diff_sq[i])\n",
     "    scaling = 1000\n",
-    "    scaled_min_square_distance = model.NewIntVar(0, 2 * scaling * room_size**2,\n",
-    "                                                 'scaled_min_square_distance')\n",
+    "    scaled_min_square_distance = model.NewIntVar(\n",
+    "        0, 2 * scaling * room_size**2, \"scaled_min_square_distance\"\n",
+    "    )\n",
     "\n",
     "    # Build intermediate variables and get the list of squared distances on\n",
     "    # each dimension.\n",
     "    for i in range(num_robots - 1):\n",
     "        for j in range(i + 1, num_robots):\n",
     "            # Compute the distance on each dimension between robot i and robot j.\n",
-    "            x_diff = model.NewIntVar(-room_size, room_size, f'x_diff{i}')\n",
-    "            y_diff = model.NewIntVar(-room_size, room_size, f'y_diff{i}')\n",
+    "            x_diff = model.NewIntVar(-room_size, room_size, f\"x_diff{i}\")\n",
+    "            y_diff = model.NewIntVar(-room_size, room_size, f\"y_diff{i}\")\n",
     "            model.Add(x_diff == x[i] - x[j])\n",
     "            model.Add(y_diff == y[i] - y[j])\n",
     "\n",
     "            # Compute the square of the previous differences.\n",
-    "            x_diff_sq = model.NewIntVar(0, room_size**2, f'x_diff_sq{i}')\n",
-    "            y_diff_sq = model.NewIntVar(0, room_size**2, f'y_diff_sq{i}')\n",
+    "            x_diff_sq = model.NewIntVar(0, room_size**2, f\"x_diff_sq{i}\")\n",
+    "            y_diff_sq = model.NewIntVar(0, room_size**2, f\"y_diff_sq{i}\")\n",
     "            model.AddMultiplicationEquality(x_diff_sq, x_diff, x_diff)\n",
     "            model.AddMultiplicationEquality(y_diff_sq, y_diff, y_diff)\n",
     "\n",
     "            # We just need to be <= to the scaled square distance as we are\n",
     "            # maximizing the min distance, which is equivalent as maximizing the min\n",
     "            # square distance.\n",
-    "            model.Add(scaled_min_square_distance <= scaling *\n",
-    "                      (x_diff_sq + y_diff_sq))\n",
+    "            model.Add(scaled_min_square_distance <= scaling * (x_diff_sq + y_diff_sq))\n",
     "\n",
     "    # Naive symmetry breaking.\n",
     "    for i in range(1, num_robots):\n",
@@ -165,17 +166,19 @@
     "\n",
     "    # Prints the solution.\n",
     "    if status == cp_model.OPTIMAL or status == cp_model.FEASIBLE:\n",
-    "        print(f'Spread {num_robots} with a min pairwise distance of'\n",
-    "              f' {math.sqrt(solver.ObjectiveValue() / scaling)}')\n",
+    "        print(\n",
+    "            f\"Spread {num_robots} with a min pairwise distance of\"\n",
+    "            f\" {math.sqrt(solver.ObjectiveValue() / scaling)}\"\n",
+    "        )\n",
     "        for i in range(num_robots):\n",
-    "            print(f'robot {i}: x={solver.Value(x[i])} y={solver.Value(y[i])}')\n",
+    "            print(f\"robot {i}: x={solver.Value(x[i])} y={solver.Value(y[i])}\")\n",
     "    else:\n",
-    "        print('No solution found.')\n",
+    "        print(\"No solution found.\")\n",
     "\n",
     "\n",
     "def main(argv: Sequence[str]) -> None:\n",
     "    if len(argv) > 1:\n",
-    "        raise app.UsageError('Too many command-line arguments.')\n",
+    "        raise app.UsageError(\"Too many command-line arguments.\")\n",
     "\n",
     "    spread_robots(_NUM_ROBOTS.value, _ROOM_SIZE.value, _PARAMS.value)\n",
     "\n",
diff --git a/examples/notebook/examples/steel_mill_slab_sat.ipynb b/examples/notebook/examples/steel_mill_slab_sat.ipynb
index 677a8f9263..d2d4391b99 100644
--- a/examples/notebook/examples/steel_mill_slab_sat.ipynb
+++ b/examples/notebook/examples/steel_mill_slab_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Solves the Stell Mill Slab problem with 4 different techniques.\n",
     "\n"
    ]
@@ -86,230 +87,94 @@
     "import collections\n",
     "import time\n",
     "\n",
+    "from google.protobuf import text_format\n",
     "from ortools.sat.python import cp_model\n",
     "\n",
-    "_PROBLEM = flags.DEFINE_integer('problem', 2, 'Problem id to solve.')\n",
+    "\n",
+    "_PROBLEM = flags.DEFINE_integer(\"problem\", 2, \"Problem id to solve.\")\n",
     "_BREAK_SYMMETRIES = flags.DEFINE_boolean(\n",
-    "    'break_symmetries', True, 'Break symmetries between equivalent orders.')\n",
+    "    \"break_symmetries\", True, \"Break symmetries between equivalent orders.\"\n",
+    ")\n",
     "_SOLVER = flags.DEFINE_string(\n",
-    "    'solver', 'sat_column', 'Method used to solve: sat, sat_table, sat_column.')\n",
+    "    \"solver\", \"sat_column\", \"Method used to solve: sat, sat_table, sat_column.\"\n",
+    ")\n",
+    "_PARAMS = flags.DEFINE_string(\n",
+    "    \"params\",\n",
+    "    \"max_time_in_seconds:20,num_workers:8,log_search_progress:true\",\n",
+    "    \"CP-SAT parameters.\",\n",
+    ")\n",
     "\n",
     "\n",
     "def build_problem(problem_id):\n",
     "    \"\"\"Build problem data.\"\"\"\n",
+    "    capacities = None\n",
+    "    num_colors = None\n",
+    "    num_slabs = None\n",
+    "    orders = None\n",
+    "\n",
     "    if problem_id == 0:\n",
     "        capacities = [\n",
-    "            0, 12, 14, 17, 18, 19, 20, 23, 24, 25, 26, 27, 28, 29, 30, 32, 35,\n",
-    "            39, 42, 43, 44\n",
+    "            # fmt:off\n",
+    "        0, 12, 14, 17, 18, 19, 20, 23, 24, 25, 26, 27, 28, 29, 30, 32, 35, 39, 42, 43, 44,\n",
+    "            # fmt:on\n",
     "        ]\n",
     "        num_colors = 88\n",
     "        num_slabs = 111\n",
-    "        orders = [\n",
-    "            (4, 1),  # (size, color)\n",
-    "            (22, 2),\n",
-    "            (9, 3),\n",
-    "            (5, 4),\n",
-    "            (8, 5),\n",
-    "            (3, 6),\n",
-    "            (3, 4),\n",
-    "            (4, 7),\n",
-    "            (7, 4),\n",
-    "            (7, 8),\n",
-    "            (3, 6),\n",
-    "            (2, 6),\n",
-    "            (2, 4),\n",
-    "            (8, 9),\n",
-    "            (5, 10),\n",
-    "            (7, 11),\n",
-    "            (4, 7),\n",
-    "            (7, 11),\n",
-    "            (5, 10),\n",
-    "            (7, 11),\n",
-    "            (8, 9),\n",
-    "            (3, 1),\n",
-    "            (25, 12),\n",
-    "            (14, 13),\n",
-    "            (3, 6),\n",
-    "            (22, 14),\n",
-    "            (19, 15),\n",
-    "            (19, 15),\n",
-    "            (22, 16),\n",
-    "            (22, 17),\n",
-    "            (22, 18),\n",
-    "            (20, 19),\n",
-    "            (22, 20),\n",
-    "            (5, 21),\n",
-    "            (4, 22),\n",
-    "            (10, 23),\n",
-    "            (26, 24),\n",
-    "            (17, 25),\n",
-    "            (20, 26),\n",
-    "            (16, 27),\n",
-    "            (10, 28),\n",
-    "            (19, 29),\n",
-    "            (10, 30),\n",
-    "            (10, 31),\n",
-    "            (23, 32),\n",
-    "            (22, 33),\n",
-    "            (26, 34),\n",
-    "            (27, 35),\n",
-    "            (22, 36),\n",
-    "            (27, 37),\n",
-    "            (22, 38),\n",
-    "            (22, 39),\n",
-    "            (13, 40),\n",
-    "            (14, 41),\n",
-    "            (16, 27),\n",
-    "            (26, 34),\n",
-    "            (26, 42),\n",
-    "            (27, 35),\n",
-    "            (22, 36),\n",
-    "            (20, 43),\n",
-    "            (26, 24),\n",
-    "            (22, 44),\n",
-    "            (13, 45),\n",
-    "            (19, 46),\n",
-    "            (20, 47),\n",
-    "            (16, 48),\n",
-    "            (15, 49),\n",
-    "            (17, 50),\n",
-    "            (10, 28),\n",
-    "            (20, 51),\n",
-    "            (5, 52),\n",
-    "            (26, 24),\n",
-    "            (19, 53),\n",
-    "            (15, 54),\n",
-    "            (10, 55),\n",
-    "            (10, 56),\n",
-    "            (13, 57),\n",
-    "            (13, 58),\n",
-    "            (13, 59),\n",
-    "            (12, 60),\n",
-    "            (12, 61),\n",
-    "            (18, 62),\n",
-    "            (10, 63),\n",
-    "            (18, 64),\n",
-    "            (16, 65),\n",
-    "            (20, 66),\n",
-    "            (12, 67),\n",
-    "            (6, 68),\n",
-    "            (6, 68),\n",
-    "            (15, 69),\n",
-    "            (15, 70),\n",
-    "            (15, 70),\n",
-    "            (21, 71),\n",
-    "            (30, 72),\n",
-    "            (30, 73),\n",
-    "            (30, 74),\n",
-    "            (30, 75),\n",
-    "            (23, 76),\n",
-    "            (15, 77),\n",
-    "            (15, 78),\n",
-    "            (27, 79),\n",
-    "            (27, 80),\n",
-    "            (27, 81),\n",
-    "            (27, 82),\n",
-    "            (27, 83),\n",
-    "            (27, 84),\n",
-    "            (27, 79),\n",
-    "            (27, 85),\n",
-    "            (27, 86),\n",
-    "            (10, 87),\n",
-    "            (3, 88)\n",
+    "        orders = [  # (size, color)\n",
+    "            # fmt:off\n",
+    "        (4, 1), (22, 2), (9, 3), (5, 4), (8, 5), (3, 6), (3, 4), (4, 7),\n",
+    "        (7, 4), (7, 8), (3, 6), (2, 6), (2, 4), (8, 9), (5, 10), (7, 11),\n",
+    "        (4, 7), (7, 11), (5, 10), (7, 11), (8, 9), (3, 1), (25, 12), (14, 13),\n",
+    "        (3, 6), (22, 14), (19, 15), (19, 15), (22, 16), (22, 17), (22, 18),\n",
+    "        (20, 19), (22, 20), (5, 21), (4, 22), (10, 23), (26, 24), (17, 25),\n",
+    "        (20, 26), (16, 27), (10, 28), (19, 29), (10, 30), (10, 31), (23, 32),\n",
+    "        (22, 33), (26, 34), (27, 35), (22, 36), (27, 37), (22, 38), (22, 39),\n",
+    "        (13, 40), (14, 41), (16, 27), (26, 34), (26, 42), (27, 35), (22, 36),\n",
+    "        (20, 43), (26, 24), (22, 44), (13, 45), (19, 46), (20, 47), (16, 48),\n",
+    "        (15, 49), (17, 50), (10, 28), (20, 51), (5, 52), (26, 24), (19, 53),\n",
+    "        (15, 54), (10, 55), (10, 56), (13, 57), (13, 58), (13, 59), (12, 60),\n",
+    "        (12, 61), (18, 62), (10, 63), (18, 64), (16, 65), (20, 66), (12, 67),\n",
+    "        (6, 68), (6, 68), (15, 69), (15, 70), (15, 70), (21, 71), (30, 72),\n",
+    "        (30, 73), (30, 74), (30, 75), (23, 76), (15, 77), (15, 78), (27, 79),\n",
+    "        (27, 80), (27, 81), (27, 82), (27, 83), (27, 84), (27, 79), (27, 85),\n",
+    "        (27, 86), (10, 87), (3, 88),\n",
+    "            # fmt:on\n",
     "        ]\n",
     "    elif problem_id == 1:\n",
     "        capacities = [0, 17, 44]\n",
     "        num_colors = 23\n",
     "        num_slabs = 30\n",
-    "        orders = [\n",
-    "            (4, 1),  # (size, color)\n",
-    "            (22, 2),\n",
-    "            (9, 3),\n",
-    "            (5, 4),\n",
-    "            (8, 5),\n",
-    "            (3, 6),\n",
-    "            (3, 4),\n",
-    "            (4, 7),\n",
-    "            (7, 4),\n",
-    "            (7, 8),\n",
-    "            (3, 6),\n",
-    "            (2, 6),\n",
-    "            (2, 4),\n",
-    "            (8, 9),\n",
-    "            (5, 10),\n",
-    "            (7, 11),\n",
-    "            (4, 7),\n",
-    "            (7, 11),\n",
-    "            (5, 10),\n",
-    "            (7, 11),\n",
-    "            (8, 9),\n",
-    "            (3, 1),\n",
-    "            (25, 12),\n",
-    "            (14, 13),\n",
-    "            (3, 6),\n",
-    "            (22, 14),\n",
-    "            (19, 15),\n",
-    "            (19, 15),\n",
-    "            (22, 16),\n",
-    "            (22, 17),\n",
-    "            (22, 18),\n",
-    "            (20, 19),\n",
-    "            (22, 20),\n",
-    "            (5, 21),\n",
-    "            (4, 22),\n",
-    "            (10, 23)\n",
+    "        orders = [  # (size, color)\n",
+    "            # fmt:off\n",
+    "        (4, 1), (22, 2), (9, 3), (5, 4), (8, 5), (3, 6), (3, 4), (4, 7), (7, 4),\n",
+    "        (7, 8), (3, 6), (2, 6), (2, 4), (8, 9), (5, 10), (7, 11), (4, 7), (7, 11),\n",
+    "        (5, 10), (7, 11), (8, 9), (3, 1), (25, 12), (14, 13), (3, 6), (22, 14),\n",
+    "        (19, 15), (19, 15), (22, 16), (22, 17), (22, 18), (20, 19), (22, 20),\n",
+    "        (5, 21), (4, 22), (10, 23),\n",
+    "            # fmt:on\n",
     "        ]\n",
     "    elif problem_id == 2:\n",
     "        capacities = [0, 17, 44]\n",
     "        num_colors = 15\n",
     "        num_slabs = 20\n",
-    "        orders = [\n",
-    "            (4, 1),  # (size, color)\n",
-    "            (22, 2),\n",
-    "            (9, 3),\n",
-    "            (5, 4),\n",
-    "            (8, 5),\n",
-    "            (3, 6),\n",
-    "            (3, 4),\n",
-    "            (4, 7),\n",
-    "            (7, 4),\n",
-    "            (7, 8),\n",
-    "            (3, 6),\n",
-    "            (2, 6),\n",
-    "            (2, 4),\n",
-    "            (8, 9),\n",
-    "            (5, 10),\n",
-    "            (7, 11),\n",
-    "            (4, 7),\n",
-    "            (7, 11),\n",
-    "            (5, 10),\n",
-    "            (7, 11),\n",
-    "            (8, 9),\n",
-    "            (3, 1),\n",
-    "            (25, 12),\n",
-    "            (14, 13),\n",
-    "            (3, 6),\n",
-    "            (22, 14),\n",
-    "            (19, 15),\n",
-    "            (19, 15)\n",
+    "        orders = [  # (size, color)\n",
+    "            # fmt:off\n",
+    "        (4, 1), (22, 2), (9, 3), (5, 4), (8, 5), (3, 6), (3, 4), (4, 7), (7, 4),\n",
+    "        (7, 8), (3, 6), (2, 6), (2, 4), (8, 9), (5, 10), (7, 11), (4, 7), (7, 11),\n",
+    "        (5, 10), (7, 11), (8, 9), (3, 1), (25, 12), (14, 13), (3, 6), (22, 14),\n",
+    "        (19, 15), (19, 15),\n",
+    "            # fmt:on\n",
     "        ]\n",
     "\n",
     "    elif problem_id == 3:\n",
     "        capacities = [0, 17, 44]\n",
     "        num_colors = 8\n",
     "        num_slabs = 10\n",
-    "        orders = [\n",
-    "            (4, 1),  # (size, color)\n",
-    "            (22, 2),\n",
-    "            (9, 3),\n",
-    "            (5, 4),\n",
-    "            (8, 5),\n",
-    "            (3, 6),\n",
-    "            (3, 4),\n",
-    "            (4, 7),\n",
-    "            (7, 4),\n",
-    "            (7, 8),\n",
-    "            (3, 6)\n",
+    "        orders = [  # (size, color)\n",
+    "            # fmt:off\n",
+    "        (4, 1), (22, 2), (9, 3), (5, 4), (8, 5), (3, 6), (3, 4), (4, 7),\n",
+    "        (7, 4), (7, 8), (3, 6),\n",
+    "            # fmt:on\n",
     "        ]\n",
     "\n",
     "    return (num_slabs, capacities, num_colors, orders)\n",
@@ -333,21 +198,29 @@
     "        \"\"\"Called on each new solution.\"\"\"\n",
     "        current_time = time.time()\n",
     "        objective = sum(self.Value(l) for l in self.__loss)\n",
-    "        print('Solution %i, time = %f s, objective = %i' %\n",
-    "              (self.__solution_count, current_time - self.__start_time,\n",
-    "               objective))\n",
+    "        print(\n",
+    "            \"Solution %i, time = %f s, objective = %i\"\n",
+    "            % (self.__solution_count, current_time - self.__start_time, objective)\n",
+    "        )\n",
     "        self.__solution_count += 1\n",
-    "        orders_in_slab = [[\n",
-    "            o for o in self.__all_orders if self.Value(self.__assign[o][s])\n",
-    "        ] for s in self.__all_slabs]\n",
+    "        orders_in_slab = [\n",
+    "            [o for o in self.__all_orders if self.Value(self.__assign[o][s])]\n",
+    "            for s in self.__all_slabs\n",
+    "        ]\n",
     "        for s in self.__all_slabs:\n",
     "            if orders_in_slab[s]:\n",
-    "                line = '  - slab %i, load = %i, loss = %i, orders = [' % (\n",
-    "                    s, self.Value(self.__load[s]), self.Value(self.__loss[s]))\n",
+    "                line = \"  - slab %i, load = %i, loss = %i, orders = [\" % (\n",
+    "                    s,\n",
+    "                    self.Value(self.__load[s]),\n",
+    "                    self.Value(self.__loss[s]),\n",
+    "                )\n",
     "                for o in orders_in_slab[s]:\n",
-    "                    line += '#%i(w%i, c%i) ' % (o, self.__orders[o][0],\n",
-    "                                                self.__orders[o][1])\n",
-    "                line += ']'\n",
+    "                    line += \"#%i(w%i, c%i) \" % (\n",
+    "                        o,\n",
+    "                        self.__orders[o][0],\n",
+    "                        self.__orders[o][1],\n",
+    "                    )\n",
+    "                line += \"]\"\n",
     "                print(line)\n",
     "\n",
     "\n",
@@ -361,16 +234,17 @@
     "    all_slabs = range(num_slabs)\n",
     "    all_colors = range(num_colors)\n",
     "    all_orders = range(len(orders))\n",
-    "    print('Solving steel mill with %i orders, %i slabs, and %i capacities' %\n",
-    "          (num_orders, num_slabs, num_capacities - 1))\n",
+    "    print(\n",
+    "        \"Solving steel mill with %i orders, %i slabs, and %i capacities\"\n",
+    "        % (num_orders, num_slabs, num_capacities - 1)\n",
+    "    )\n",
     "\n",
     "    # Compute auxiliary data.\n",
     "    widths = [x[0] for x in orders]\n",
     "    colors = [x[1] for x in orders]\n",
     "    max_capacity = max(capacities)\n",
     "    loss_array = [\n",
-    "        min(x for x in capacities if x >= c) - c for c in range(max_capacity +\n",
-    "                                                                1)\n",
+    "        min(x for x in capacities if x >= c) - c for c in range(max_capacity + 1)\n",
     "    ]\n",
     "    max_loss = max(loss_array)\n",
     "    orders_per_color = [\n",
@@ -384,16 +258,15 @@
     "\n",
     "    # Create the model and the decision variables.\n",
     "    model = cp_model.CpModel()\n",
-    "    assign = [[\n",
-    "        model.NewBoolVar('assign_%i_to_slab_%i' % (o, s)) for s in all_slabs\n",
-    "    ] for o in all_orders]\n",
-    "    loads = [\n",
-    "        model.NewIntVar(0, max_capacity, 'load_of_slab_%i' % s)\n",
+    "    assign = [\n",
+    "        [model.NewBoolVar(\"assign_%i_to_slab_%i\" % (o, s)) for s in all_slabs]\n",
+    "        for o in all_orders\n",
+    "    ]\n",
+    "    loads = [model.NewIntVar(0, max_capacity, \"load_of_slab_%i\" % s) for s in all_slabs]\n",
+    "    color_is_in_slab = [\n",
+    "        [model.NewBoolVar(\"color_%i_in_slab_%i\" % (c + 1, s)) for c in all_colors]\n",
     "        for s in all_slabs\n",
     "    ]\n",
-    "    color_is_in_slab = [[\n",
-    "        model.NewBoolVar('color_%i_in_slab_%i' % (c + 1, s)) for c in all_colors\n",
-    "    ] for s in all_slabs]\n",
     "\n",
     "    # Compute load of all slabs.\n",
     "    for s in all_slabs:\n",
@@ -411,8 +284,7 @@
     "        for s in all_slabs:\n",
     "            for o in orders_per_color[c]:\n",
     "                model.AddImplication(assign[o][s], color_is_in_slab[s][c])\n",
-    "                model.AddImplication(color_is_in_slab[s][c].Not(),\n",
-    "                                     assign[o][s].Not())\n",
+    "                model.AddImplication(color_is_in_slab[s][c].Not(), assign[o][s].Not())\n",
     "\n",
     "    # At most two colors per slab.\n",
     "    for s in all_slabs:\n",
@@ -447,35 +319,39 @@
     "                if w not in local_width_to_order:\n",
     "                    local_width_to_order[w] = []\n",
     "                local_width_to_order[w].append(o)\n",
-    "            for w, os in local_width_to_order.items():\n",
+    "            for _, os in local_width_to_order.items():\n",
     "                if len(os) > 1:\n",
     "                    for p in range(len(os) - 1):\n",
     "                        ordered_equivalent_orders.append((os[p], os[p + 1]))\n",
-    "    for w, os in width_to_unique_color_order.items():\n",
+    "    for _, os in width_to_unique_color_order.items():\n",
     "        if len(os) > 1:\n",
     "            for p in range(len(os) - 1):\n",
     "                ordered_equivalent_orders.append((os[p], os[p + 1]))\n",
     "\n",
     "    # Create position variables if there are symmetries to be broken.\n",
     "    if break_symmetries and ordered_equivalent_orders:\n",
-    "        print('  - creating %i symmetry breaking constraints' %\n",
-    "              len(ordered_equivalent_orders))\n",
+    "        print(\n",
+    "            \"  - creating %i symmetry breaking constraints\"\n",
+    "            % len(ordered_equivalent_orders)\n",
+    "        )\n",
     "        positions = {}\n",
     "        for p in ordered_equivalent_orders:\n",
     "            if p[0] not in positions:\n",
-    "                positions[p[0]] = model.NewIntVar(0, num_slabs - 1,\n",
-    "                                                  'position_of_slab_%i' % p[0])\n",
+    "                positions[p[0]] = model.NewIntVar(\n",
+    "                    0, num_slabs - 1, \"position_of_slab_%i\" % p[0]\n",
+    "                )\n",
     "                model.AddMapDomain(positions[p[0]], assign[p[0]])\n",
     "            if p[1] not in positions:\n",
-    "                positions[p[1]] = model.NewIntVar(0, num_slabs - 1,\n",
-    "                                                  'position_of_slab_%i' % p[1])\n",
+    "                positions[p[1]] = model.NewIntVar(\n",
+    "                    0, num_slabs - 1, \"position_of_slab_%i\" % p[1]\n",
+    "                )\n",
     "                model.AddMapDomain(positions[p[1]], assign[p[1]])\n",
     "            # Finally add the symmetry breaking constraint.\n",
     "            model.Add(positions[p[0]] <= positions[p[1]])\n",
     "\n",
     "    # Objective.\n",
-    "    obj = model.NewIntVar(0, num_slabs * max_loss, 'obj')\n",
-    "    losses = [model.NewIntVar(0, max_loss, 'loss_%i' % s) for s in all_slabs]\n",
+    "    obj = model.NewIntVar(0, num_slabs * max_loss, \"obj\")\n",
+    "    losses = [model.NewIntVar(0, max_loss, \"loss_%i\" % s) for s in all_slabs]\n",
     "    for s in all_slabs:\n",
     "        model.AddElement(loads[s], loss_array, losses[s])\n",
     "    model.Add(obj == sum(losses))\n",
@@ -483,17 +359,19 @@
     "\n",
     "    ### Solve model.\n",
     "    solver = cp_model.CpSolver()\n",
-    "    solver.parameters.num_search_workers = 8\n",
+    "    if _PARAMS.value:\n",
+    "        text_format.Parse(_PARAMS.value, solver.parameters)\n",
     "    objective_printer = cp_model.ObjectiveSolutionPrinter()\n",
     "    status = solver.Solve(model, objective_printer)\n",
     "\n",
     "    ### Output the solution.\n",
     "    if status in (cp_model.OPTIMAL, cp_model.FEASIBLE):\n",
     "        print(\n",
-    "            'Loss = %i, time = %f s, %i conflicts' %\n",
-    "            (solver.ObjectiveValue(), solver.WallTime(), solver.NumConflicts()))\n",
+    "            \"Loss = %i, time = %f s, %i conflicts\"\n",
+    "            % (solver.ObjectiveValue(), solver.WallTime(), solver.NumConflicts())\n",
+    "        )\n",
     "    else:\n",
-    "        print('No solution')\n",
+    "        print(\"No solution\")\n",
     "\n",
     "\n",
     "def collect_valid_slabs_dp(capacities, colors, widths, loss_array):\n",
@@ -502,13 +380,11 @@
     "\n",
     "    max_capacity = max(capacities)\n",
     "\n",
-    "    valid_assignment = collections.namedtuple('valid_assignment',\n",
-    "                                              'orders load colors')\n",
+    "    valid_assignment = collections.namedtuple(\"valid_assignment\", \"orders load colors\")\n",
     "    all_valid_assignments = [valid_assignment(orders=[], load=0, colors=[])]\n",
     "\n",
-    "    for order_id in range(len(colors)):\n",
+    "    for order_id, new_color in enumerate(colors):\n",
     "        new_width = widths[order_id]\n",
-    "        new_color = colors[order_id]\n",
     "        new_assignments = []\n",
     "        for assignment in all_valid_assignments:\n",
     "            if assignment.load + new_width > max_capacity:\n",
@@ -518,18 +394,21 @@
     "                new_colors.append(new_color)\n",
     "            if len(new_colors) > 2:\n",
     "                continue\n",
-    "            new_assignment = valid_assignment(orders=assignment.orders +\n",
-    "                                              [order_id],\n",
-    "                                              load=assignment.load + new_width,\n",
-    "                                              colors=new_colors)\n",
+    "            new_assignment = valid_assignment(\n",
+    "                orders=assignment.orders + [order_id],\n",
+    "                load=assignment.load + new_width,\n",
+    "                colors=new_colors,\n",
+    "            )\n",
     "            new_assignments.append(new_assignment)\n",
     "        all_valid_assignments.extend(new_assignments)\n",
     "\n",
-    "    print('%i assignments created in %.2f s' %\n",
-    "          (len(all_valid_assignments), time.time() - start_time))\n",
+    "    print(\n",
+    "        \"%i assignments created in %.2f s\"\n",
+    "        % (len(all_valid_assignments), time.time() - start_time)\n",
+    "    )\n",
     "    tuples = []\n",
     "    for assignment in all_valid_assignments:\n",
-    "        solution = [0 for _ in range(len(colors))]\n",
+    "        solution = [0] * len(colors)\n",
     "        for i in assignment.orders:\n",
     "            solution[i] = 1\n",
     "        solution.append(loss_array[assignment.load])\n",
@@ -549,16 +428,17 @@
     "    all_slabs = range(num_slabs)\n",
     "    all_colors = range(num_colors)\n",
     "    all_orders = range(len(orders))\n",
-    "    print('Solving steel mill with %i orders, %i slabs, and %i capacities' %\n",
-    "          (num_orders, num_slabs, num_capacities - 1))\n",
+    "    print(\n",
+    "        \"Solving steel mill with %i orders, %i slabs, and %i capacities\"\n",
+    "        % (num_orders, num_slabs, num_capacities - 1)\n",
+    "    )\n",
     "\n",
     "    # Compute auxiliary data.\n",
     "    widths = [x[0] for x in orders]\n",
     "    colors = [x[1] for x in orders]\n",
     "    max_capacity = max(capacities)\n",
     "    loss_array = [\n",
-    "        min(x for x in capacities if x >= c) - c for c in range(max_capacity +\n",
-    "                                                                1)\n",
+    "        min(x for x in capacities if x >= c) - c for c in range(max_capacity + 1)\n",
     "    ]\n",
     "    max_loss = max(loss_array)\n",
     "\n",
@@ -566,21 +446,23 @@
     "\n",
     "    # Create the model and the decision variables.\n",
     "    model = cp_model.CpModel()\n",
-    "    assign = [[\n",
-    "        model.NewBoolVar('assign_%i_to_slab_%i' % (o, s)) for s in all_slabs\n",
-    "    ] for o in all_orders]\n",
-    "    loads = [model.NewIntVar(0, max_capacity, 'load_%i' % s) for s in all_slabs]\n",
-    "    losses = [model.NewIntVar(0, max_loss, 'loss_%i' % s) for s in all_slabs]\n",
+    "    assign = [\n",
+    "        [model.NewBoolVar(\"assign_%i_to_slab_%i\" % (o, s)) for s in all_slabs]\n",
+    "        for o in all_orders\n",
+    "    ]\n",
+    "    loads = [model.NewIntVar(0, max_capacity, \"load_%i\" % s) for s in all_slabs]\n",
+    "    losses = [model.NewIntVar(0, max_loss, \"loss_%i\" % s) for s in all_slabs]\n",
     "\n",
-    "    unsorted_valid_slabs = collect_valid_slabs_dp(capacities, colors, widths,\n",
-    "                                                  loss_array)\n",
+    "    unsorted_valid_slabs = collect_valid_slabs_dp(\n",
+    "        capacities, colors, widths, loss_array\n",
+    "    )\n",
     "    # Sort slab by descending load/loss. Remove duplicates.\n",
-    "    valid_slabs = sorted(unsorted_valid_slabs,\n",
-    "                         key=lambda c: 1000 * c[-1] + c[-2])\n",
+    "    valid_slabs = sorted(unsorted_valid_slabs, key=lambda c: 1000 * c[-1] + c[-2])\n",
     "\n",
     "    for s in all_slabs:\n",
-    "        model.AddAllowedAssignments([assign[o][s] for o in all_orders] +\n",
-    "                                    [losses[s], loads[s]], valid_slabs)\n",
+    "        model.AddAllowedAssignments(\n",
+    "            [assign[o][s] for o in all_orders] + [losses[s], loads[s]], valid_slabs\n",
+    "        )\n",
     "\n",
     "    # Orders are assigned to one slab.\n",
     "    for o in all_orders:\n",
@@ -595,7 +477,7 @@
     "\n",
     "    # Collect equivalent orders.\n",
     "    if break_symmetries:\n",
-    "        print('Breaking symmetries')\n",
+    "        print(\"Breaking symmetries\")\n",
     "        width_to_unique_color_order = {}\n",
     "        ordered_equivalent_orders = []\n",
     "        orders_per_color = [\n",
@@ -619,28 +501,32 @@
     "                    if w not in local_width_to_order:\n",
     "                        local_width_to_order[w] = []\n",
     "                        local_width_to_order[w].append(o)\n",
-    "                for w, os in local_width_to_order.items():\n",
+    "                for _, os in local_width_to_order.items():\n",
     "                    if len(os) > 1:\n",
     "                        for p in range(len(os) - 1):\n",
     "                            ordered_equivalent_orders.append((os[p], os[p + 1]))\n",
-    "        for w, os in width_to_unique_color_order.items():\n",
+    "        for _, os in width_to_unique_color_order.items():\n",
     "            if len(os) > 1:\n",
     "                for p in range(len(os) - 1):\n",
     "                    ordered_equivalent_orders.append((os[p], os[p + 1]))\n",
     "\n",
     "        # Create position variables if there are symmetries to be broken.\n",
     "        if ordered_equivalent_orders:\n",
-    "            print('  - creating %i symmetry breaking constraints' %\n",
-    "                  len(ordered_equivalent_orders))\n",
+    "            print(\n",
+    "                \"  - creating %i symmetry breaking constraints\"\n",
+    "                % len(ordered_equivalent_orders)\n",
+    "            )\n",
     "            positions = {}\n",
     "            for p in ordered_equivalent_orders:\n",
     "                if p[0] not in positions:\n",
     "                    positions[p[0]] = model.NewIntVar(\n",
-    "                        0, num_slabs - 1, 'position_of_slab_%i' % p[0])\n",
+    "                        0, num_slabs - 1, \"position_of_slab_%i\" % p[0]\n",
+    "                    )\n",
     "                    model.AddMapDomain(positions[p[0]], assign[p[0]])\n",
     "                if p[1] not in positions:\n",
     "                    positions[p[1]] = model.NewIntVar(\n",
-    "                        0, num_slabs - 1, 'position_of_slab_%i' % p[1])\n",
+    "                        0, num_slabs - 1, \"position_of_slab_%i\" % p[1]\n",
+    "                    )\n",
     "                    model.AddMapDomain(positions[p[1]], assign[p[1]])\n",
     "                    # Finally add the symmetry breaking constraint.\n",
     "                model.Add(positions[p[0]] <= positions[p[1]])\n",
@@ -648,22 +534,24 @@
     "    # Objective.\n",
     "    model.Minimize(sum(losses))\n",
     "\n",
-    "    print('Model created')\n",
+    "    print(\"Model created\")\n",
     "\n",
     "    ### Solve model.\n",
     "    solver = cp_model.CpSolver()\n",
-    "    solver.num_search_workers = 8\n",
-    "    solution_printer = SteelMillSlabSolutionPrinter(orders, assign, loads,\n",
-    "                                                    losses)\n",
+    "    if _PARAMS.value:\n",
+    "        text_format.Parse(_PARAMS.value, solver.parameters)\n",
+    "\n",
+    "    solution_printer = SteelMillSlabSolutionPrinter(orders, assign, loads, losses)\n",
     "    status = solver.Solve(model, solution_printer)\n",
     "\n",
     "    ### Output the solution.\n",
     "    if status == cp_model.OPTIMAL:\n",
     "        print(\n",
-    "            'Loss = %i, time = %.2f s, %i conflicts' %\n",
-    "            (solver.ObjectiveValue(), solver.WallTime(), solver.NumConflicts()))\n",
+    "            \"Loss = %i, time = %.2f s, %i conflicts\"\n",
+    "            % (solver.ObjectiveValue(), solver.WallTime(), solver.NumConflicts())\n",
+    "        )\n",
     "    else:\n",
-    "        print('No solution')\n",
+    "        print(\"No solution\")\n",
     "\n",
     "\n",
     "def steel_mill_slab_with_column_generation(problem):\n",
@@ -674,76 +562,76 @@
     "    num_orders = len(orders)\n",
     "    num_capacities = len(capacities)\n",
     "    all_orders = range(len(orders))\n",
-    "    print('Solving steel mill with %i orders, %i slabs, and %i capacities' %\n",
-    "          (num_orders, num_slabs, num_capacities - 1))\n",
+    "    print(\n",
+    "        \"Solving steel mill with %i orders, %i slabs, and %i capacities\"\n",
+    "        % (num_orders, num_slabs, num_capacities - 1)\n",
+    "    )\n",
     "\n",
     "    # Compute auxiliary data.\n",
     "    widths = [x[0] for x in orders]\n",
     "    colors = [x[1] for x in orders]\n",
     "    max_capacity = max(capacities)\n",
     "    loss_array = [\n",
-    "        min(x for x in capacities if x >= c) - c for c in range(max_capacity +\n",
-    "                                                                1)\n",
+    "        min(x for x in capacities if x >= c) - c for c in range(max_capacity + 1)\n",
     "    ]\n",
     "\n",
     "    ### Model problem.\n",
     "\n",
     "    # Generate all valid slabs (columns)\n",
-    "    unsorted_valid_slabs = collect_valid_slabs_dp(capacities, colors, widths,\n",
-    "                                                  loss_array)\n",
+    "    unsorted_valid_slabs = collect_valid_slabs_dp(\n",
+    "        capacities, colors, widths, loss_array\n",
+    "    )\n",
     "\n",
     "    # Sort slab by descending load/loss. Remove duplicates.\n",
-    "    valid_slabs = sorted(unsorted_valid_slabs,\n",
-    "                         key=lambda c: 1000 * c[-1] + c[-2])\n",
+    "    valid_slabs = sorted(unsorted_valid_slabs, key=lambda c: 1000 * c[-1] + c[-2])\n",
     "    all_valid_slabs = range(len(valid_slabs))\n",
     "\n",
     "    # create model and decision variables.\n",
     "    model = cp_model.CpModel()\n",
-    "    selected = [model.NewBoolVar('selected_%i' % i) for i in all_valid_slabs]\n",
+    "    selected = [model.NewBoolVar(\"selected_%i\" % i) for i in all_valid_slabs]\n",
     "\n",
     "    for order_id in all_orders:\n",
     "        model.Add(\n",
-    "            sum(selected[i]\n",
-    "                for i, slab in enumerate(valid_slabs)\n",
-    "                if slab[order_id]) == 1)\n",
+    "            sum(selected[i] for i, slab in enumerate(valid_slabs) if slab[order_id])\n",
+    "            == 1\n",
+    "        )\n",
     "\n",
     "    # Redundant constraint (sum of loads == sum of widths).\n",
     "    model.Add(\n",
-    "        sum(selected[i] * valid_slabs[i][-1]\n",
-    "            for i in all_valid_slabs) == sum(widths))\n",
+    "        sum(selected[i] * valid_slabs[i][-1] for i in all_valid_slabs) == sum(widths)\n",
+    "    )\n",
     "\n",
     "    # Objective.\n",
-    "    model.Minimize(\n",
-    "        sum(selected[i] * valid_slabs[i][-2] for i in all_valid_slabs))\n",
+    "    model.Minimize(sum(selected[i] * valid_slabs[i][-2] for i in all_valid_slabs))\n",
     "\n",
-    "    print('Model created')\n",
+    "    print(\"Model created\")\n",
     "\n",
     "    ### Solve model.\n",
     "    solver = cp_model.CpSolver()\n",
-    "    solver.parameters.num_search_workers = 8\n",
-    "    solver.parameters.log_search_progress = True\n",
+    "    if _PARAMS.value:\n",
+    "        text_format.Parse(_PARAMS.value, solver.parameters)\n",
     "    solution_printer = cp_model.ObjectiveSolutionPrinter()\n",
     "    status = solver.Solve(model, solution_printer)\n",
     "\n",
     "    ### Output the solution.\n",
     "    if status in (cp_model.OPTIMAL, cp_model.FEASIBLE):\n",
     "        print(\n",
-    "            'Loss = %i, time = %.2f s, %i conflicts' %\n",
-    "            (solver.ObjectiveValue(), solver.WallTime(), solver.NumConflicts()))\n",
+    "            \"Loss = %i, time = %.2f s, %i conflicts\"\n",
+    "            % (solver.ObjectiveValue(), solver.WallTime(), solver.NumConflicts())\n",
+    "        )\n",
     "    else:\n",
-    "        print('No solution')\n",
+    "        print(\"No solution\")\n",
     "\n",
     "\n",
     "def main(_):\n",
-    "    if _SOLVER.value == 'sat':\n",
+    "    if _SOLVER.value == \"sat\":\n",
     "        steel_mill_slab(_PROBLEM.value, _BREAK_SYMMETRIES.value)\n",
-    "    elif _SOLVER.value == 'sat_table':\n",
-    "        steel_mill_slab_with_valid_slabs(_PROBLEM.value,\n",
-    "                                         _BREAK_SYMMETRIES.value)\n",
-    "    elif _SOLVER.value == 'sat_column':\n",
+    "    elif _SOLVER.value == \"sat_table\":\n",
+    "        steel_mill_slab_with_valid_slabs(_PROBLEM.value, _BREAK_SYMMETRIES.value)\n",
+    "    elif _SOLVER.value == \"sat_column\":\n",
     "        steel_mill_slab_with_column_generation(_PROBLEM.value)\n",
     "    else:\n",
-    "        print(f'Unknown model {_SOLVER.value}')\n",
+    "        print(f\"Unknown model {_SOLVER.value}\")\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/examples/sudoku_sat.ipynb b/examples/notebook/examples/sudoku_sat.ipynb
index 2ed1f17253..aac0afad93 100644
--- a/examples/notebook/examples/sudoku_sat.ipynb
+++ b/examples/notebook/examples/sudoku_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "This model implements a sudoku solver.\n"
    ]
   },
@@ -95,16 +96,22 @@
     "    line = list(range(0, line_size))\n",
     "    cell = list(range(0, cell_size))\n",
     "\n",
-    "    initial_grid = [[0, 6, 0, 0, 5, 0, 0, 2, 0], [0, 0, 0, 3, 0, 0, 0, 9, 0],\n",
-    "                    [7, 0, 0, 6, 0, 0, 0, 1, 0], [0, 0, 6, 0, 3, 0, 4, 0, 0],\n",
-    "                    [0, 0, 4, 0, 7, 0, 1, 0, 0], [0, 0, 5, 0, 9, 0, 8, 0, 0],\n",
-    "                    [0, 4, 0, 0, 0, 1, 0, 0, 6], [0, 3, 0, 0, 0, 8, 0, 0, 0],\n",
-    "                    [0, 2, 0, 0, 4, 0, 0, 5, 0]]\n",
+    "    initial_grid = [\n",
+    "        [0, 6, 0, 0, 5, 0, 0, 2, 0],\n",
+    "        [0, 0, 0, 3, 0, 0, 0, 9, 0],\n",
+    "        [7, 0, 0, 6, 0, 0, 0, 1, 0],\n",
+    "        [0, 0, 6, 0, 3, 0, 4, 0, 0],\n",
+    "        [0, 0, 4, 0, 7, 0, 1, 0, 0],\n",
+    "        [0, 0, 5, 0, 9, 0, 8, 0, 0],\n",
+    "        [0, 4, 0, 0, 0, 1, 0, 0, 6],\n",
+    "        [0, 3, 0, 0, 0, 8, 0, 0, 0],\n",
+    "        [0, 2, 0, 0, 4, 0, 0, 5, 0],\n",
+    "    ]\n",
     "\n",
     "    grid = {}\n",
     "    for i in line:\n",
     "        for j in line:\n",
-    "            grid[(i, j)] = model.NewIntVar(1, line_size, 'grid %i %i' % (i, j))\n",
+    "            grid[(i, j)] = model.NewIntVar(1, line_size, \"grid %i %i\" % (i, j))\n",
     "\n",
     "    # AllDifferent on rows.\n",
     "    for i in line:\n",
@@ -120,8 +127,7 @@
     "            one_cell = []\n",
     "            for di in cell:\n",
     "                for dj in cell:\n",
-    "                    one_cell.append(grid[(i * cell_size + di,\n",
-    "                                          j * cell_size + dj)])\n",
+    "                    one_cell.append(grid[(i * cell_size + di, j * cell_size + dj)])\n",
     "\n",
     "            model.AddAllDifferent(one_cell)\n",
     "\n",
diff --git a/examples/notebook/examples/task_allocation_sat.ipynb b/examples/notebook/examples/task_allocation_sat.ipynb
index 986dfe664d..829f048a32 100644
--- a/examples/notebook/examples/task_allocation_sat.ipynb
+++ b/examples/notebook/examples/task_allocation_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "CP-SAT model for task allocation problem.\n",
     "\n",
     "see http://yetanothermathprogrammingconsultant.blogspot.com/2018/09/minizinc-\n",
@@ -93,256 +94,210 @@
     "def task_allocation_sat():\n",
     "    \"\"\"Solves the task allocation problem.\"\"\"\n",
     "    # Availability matrix.\n",
-    "    available = [[\n",
-    "        0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "        0, 0\n",
+    "    available = [\n",
+    "        # fmt:off\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
     "    ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 1, 1, 1, 1, 1, 1, 1, 1\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n",
-    "                     1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 1, 1, 1, 1, 1, 1, 1, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 1\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 1\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
-    "                     1, 1, 1, 1, 1, 1, 1, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n",
-    "                     1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n",
-    "                     1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 1, 1, 1, 1, 1, 1, 1, 1, 1\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
-    "                     1, 1, 1, 1, 1, 1, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 1, 1, 1, 1, 1, 1, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,\n",
-    "                     1, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,\n",
-    "                     1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
-    "                     1, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ],\n",
-    "                 [\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1,\n",
-    "                     1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-    "                     0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
-    "                 ]]\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+    "        0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+    "        1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,\n",
+    "        1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+    "        1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
+    "        1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1,\n",
+    "        1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "    [\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,\n",
+    "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n",
+    "    ],\n",
+    "        # fmt:on\n",
+    "    ]\n",
     "\n",
     "    ntasks = len(available)\n",
     "    nslots = len(available[0])\n",
@@ -359,30 +314,31 @@
     "    assign = {}\n",
     "    for task in all_tasks:\n",
     "        for slot in all_slots:\n",
-    "            assign[(task, slot)] = model.NewBoolVar('x[%i][%i]' % (task, slot))\n",
-    "    count = model.NewIntVar(0, nslots, 'count')\n",
-    "    slot_used = [model.NewBoolVar('slot_used[%i]' % s) for s in all_slots]\n",
+    "            assign[(task, slot)] = model.NewBoolVar(\"x[%i][%i]\" % (task, slot))\n",
+    "    count = model.NewIntVar(0, nslots, \"count\")\n",
+    "    slot_used = [model.NewBoolVar(\"slot_used[%i]\" % s) for s in all_slots]\n",
     "\n",
     "    for task in all_tasks:\n",
     "        model.Add(\n",
-    "            sum(assign[(task, slot)]\n",
-    "                for slot in all_slots\n",
-    "                if available[task][slot] == 1) == 1)\n",
+    "            sum(\n",
+    "                assign[(task, slot)] for slot in all_slots if available[task][slot] == 1\n",
+    "            )\n",
+    "            == 1\n",
+    "        )\n",
     "\n",
     "    for slot in all_slots:\n",
     "        model.Add(\n",
-    "            sum(assign[(task, slot)]\n",
-    "                for task in all_tasks\n",
-    "                if available[task][slot] == 1) <= capacity)\n",
-    "        model.AddBoolOr([\n",
-    "            assign[(task, slot)]\n",
-    "            for task in all_tasks\n",
-    "            if available[task][slot] == 1\n",
-    "        ]).OnlyEnforceIf(slot_used[slot])\n",
+    "            sum(\n",
+    "                assign[(task, slot)] for task in all_tasks if available[task][slot] == 1\n",
+    "            )\n",
+    "            <= capacity\n",
+    "        )\n",
+    "        model.AddBoolOr(\n",
+    "            [assign[(task, slot)] for task in all_tasks if available[task][slot] == 1]\n",
+    "        ).OnlyEnforceIf(slot_used[slot])\n",
     "        for task in all_tasks:\n",
     "            if available[task][slot] == 1:\n",
-    "                model.AddImplication(slot_used[slot].Not(),\n",
-    "                                     assign[(task, slot)].Not())\n",
+    "                model.AddImplication(slot_used[slot].Not(), assign[(task, slot)].Not())\n",
     "            else:\n",
     "                model.Add(assign[(task, slot)] == 0)\n",
     "\n",
@@ -399,15 +355,15 @@
     "    solver.parameters.num_search_workers = 16\n",
     "    status = solver.Solve(model)\n",
     "\n",
-    "    print('Statistics')\n",
-    "    print('  - status =', solver.StatusName(status))\n",
-    "    print('  - optimal solution =', solver.ObjectiveValue())\n",
-    "    print('  - wall time : %f s' % solver.WallTime())\n",
+    "    print(\"Statistics\")\n",
+    "    print(\"  - status =\", solver.StatusName(status))\n",
+    "    print(\"  - optimal solution =\", solver.ObjectiveValue())\n",
+    "    print(\"  - wall time : %f s\" % solver.WallTime())\n",
     "\n",
     "\n",
     "def main(argv: Sequence[str]) -> None:\n",
     "    if len(argv) > 1:\n",
-    "        raise app.UsageError('Too many command-line arguments.')\n",
+    "        raise app.UsageError(\"Too many command-line arguments.\")\n",
     "    task_allocation_sat()\n",
     "\n",
     "\n",
diff --git a/examples/notebook/examples/tasks_and_workers_assignment_sat.ipynb b/examples/notebook/examples/tasks_and_workers_assignment_sat.ipynb
index d6946484e9..a79312e924 100644
--- a/examples/notebook/examples/tasks_and_workers_assignment_sat.ipynb
+++ b/examples/notebook/examples/tasks_and_workers_assignment_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Tasks and workers to group assignment to average sum(cost) / #workers.\n"
    ]
   },
@@ -94,8 +95,10 @@
     "        self.__solution_count = 0\n",
     "\n",
     "    def on_solution_callback(self):\n",
-    "        print('Solution %i, time = %f s, objective = %i' %\n",
-    "              (self.__solution_count, self.WallTime(), self.ObjectiveValue()))\n",
+    "        print(\n",
+    "            \"Solution %i, time = %f s, objective = %i\"\n",
+    "            % (self.__solution_count, self.WallTime(), self.ObjectiveValue())\n",
+    "        )\n",
     "        self.__solution_count += 1\n",
     "\n",
     "\n",
@@ -118,13 +121,13 @@
     "    x = {}\n",
     "    for i in all_workers:\n",
     "        for j in all_groups:\n",
-    "            x[i, j] = model.NewBoolVar('x[%i,%i]' % (i, j))\n",
+    "            x[i, j] = model.NewBoolVar(\"x[%i,%i]\" % (i, j))\n",
     "\n",
     "    ## y_kj is 1 if task k is assigned to group j\n",
     "    y = {}\n",
     "    for k in all_tasks:\n",
     "        for j in all_groups:\n",
-    "            y[k, j] = model.NewBoolVar('x[%i,%i]' % (k, j))\n",
+    "            y[k, j] = model.NewBoolVar(\"x[%i,%i]\" % (k, j))\n",
     "\n",
     "    # Constraints\n",
     "\n",
@@ -143,13 +146,11 @@
     "    scaled_sum_of_costs_in_group = []\n",
     "    scaling = 1000  # We introduce scaling to deal with floating point average.\n",
     "    for j in all_groups:\n",
-    "        n = model.NewIntVar(1, num_workers, 'num_workers_in_group_%i' % j)\n",
+    "        n = model.NewIntVar(1, num_workers, \"num_workers_in_group_%i\" % j)\n",
     "        model.Add(n == sum(x[i, j] for i in all_workers))\n",
-    "        c = model.NewIntVar(0, sum_of_costs * scaling,\n",
-    "                            'sum_of_costs_of_group_%i' % j)\n",
+    "        c = model.NewIntVar(0, sum_of_costs * scaling, \"sum_of_costs_of_group_%i\" % j)\n",
     "        model.Add(c == sum(y[k, j] * task_cost[k] * scaling for k in all_tasks))\n",
-    "        a = model.NewIntVar(0, sum_of_costs * scaling,\n",
-    "                            'average_cost_of_group_%i' % j)\n",
+    "        a = model.NewIntVar(0, sum_of_costs * scaling, \"average_cost_of_group_%i\" % j)\n",
     "        model.AddDivisionEquality(a, c, n)\n",
     "\n",
     "        averages.append(a)\n",
@@ -160,7 +161,7 @@
     "    model.Add(sum(num_workers_in_group) == num_workers)\n",
     "\n",
     "    # Objective.\n",
-    "    obj = model.NewIntVar(0, sum_of_costs * scaling, 'obj')\n",
+    "    obj = model.NewIntVar(0, sum_of_costs * scaling, \"obj\")\n",
     "    model.AddMaxEquality(obj, averages)\n",
     "    model.Minimize(obj)\n",
     "\n",
@@ -173,17 +174,18 @@
     "\n",
     "    if status == cp_model.OPTIMAL:\n",
     "        for j in all_groups:\n",
-    "            print('Group %i' % j)\n",
+    "            print(\"Group %i\" % j)\n",
     "            for i in all_workers:\n",
     "                if solver.BooleanValue(x[i, j]):\n",
-    "                    print('  - worker %i' % i)\n",
+    "                    print(\"  - worker %i\" % i)\n",
     "            for k in all_tasks:\n",
     "                if solver.BooleanValue(y[k, j]):\n",
-    "                    print('  - task %i with cost %i' % (k, task_cost[k]))\n",
-    "            print('  - sum_of_costs = %i' %\n",
-    "                  (solver.Value(scaled_sum_of_costs_in_group[j]) // scaling))\n",
-    "            print('  - average cost = %f' %\n",
-    "                  (solver.Value(averages[j]) * 1.0 / scaling))\n",
+    "                    print(\"  - task %i with cost %i\" % (k, task_cost[k]))\n",
+    "            print(\n",
+    "                \"  - sum_of_costs = %i\"\n",
+    "                % (solver.Value(scaled_sum_of_costs_in_group[j]) // scaling)\n",
+    "            )\n",
+    "            print(\"  - average cost = %f\" % (solver.Value(averages[j]) * 1.0 / scaling))\n",
     "\n",
     "\n",
     "tasks_and_workers_assignment_sat()\n",
@@ -191,7 +193,7 @@
     "\n",
     "def main(argv: Sequence[str]) -> None:\n",
     "    if len(argv) > 1:\n",
-    "        raise app.UsageError('Too many command-line arguments.')\n",
+    "        raise app.UsageError(\"Too many command-line arguments.\")\n",
     "    tasks_and_workers_assignment_sat()\n",
     "\n",
     "\n",
diff --git a/examples/notebook/examples/tsp_sat.ipynb b/examples/notebook/examples/tsp_sat.ipynb
index dfcae4ed6a..74f5095d74 100644
--- a/examples/notebook/examples/tsp_sat.ipynb
+++ b/examples/notebook/examples/tsp_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Simple travelling salesman problem between cities.\n"
    ]
   },
@@ -85,6 +86,7 @@
     "from ortools.sat.python import cp_model\n",
     "\n",
     "DISTANCE_MATRIX = [\n",
+    "    # fmt:off\n",
     "    [0, 10938, 4542, 2835, 29441, 2171, 1611, 9208, 9528, 11111, 16120, 22606, 22127, 20627, 21246, 23387, 16697, 33609, 26184, 24772, 22644, 20655, 30492, 23296, 32979, 18141, 19248, 17129, 17192, 15645, 12658, 11210, 12094, 13175, 18162, 4968, 12308, 10084, 13026, 15056],\n",
     "    [10938, 0, 6422, 9742, 18988, 12974, 11216, 19715, 19004, 18271, 25070, 31971, 31632, 30571, 31578, 33841, 27315, 43964, 36944, 35689, 33569, 31481, 41360, 33760, 43631, 28730, 29976, 27803, 28076, 26408, 23504, 22025, 22000, 13197, 14936, 15146, 23246, 20956, 23963, 25994],\n",
     "    [4542, 6422, 0, 3644, 25173, 6552, 5092, 13584, 13372, 13766, 19805, 26537, 26117, 24804, 25590, 27784, 21148, 37981, 30693, 29315, 27148, 25071, 34943, 27472, 37281, 22389, 23592, 21433, 21655, 20011, 17087, 15612, 15872, 11653, 15666, 8842, 16843, 14618, 17563, 19589],\n",
@@ -125,14 +127,15 @@
     "    [10084, 20956, 14618, 12135, 38935, 8306, 9793, 2615, 5850, 10467, 9918, 14568, 13907, 11803, 11750, 13657, 6901, 23862, 16125, 14748, 12981, 11624, 21033, 15358, 24144, 10304, 10742, 9094, 8042, 7408, 4580, 4072, 8446, 20543, 26181, 7668, 2747, 0, 3330, 5313],\n",
     "    [13026, 23963, 17563, 14771, 42160, 11069, 12925, 5730, 8778, 13375, 11235, 14366, 13621, 11188, 10424, 11907, 5609, 21861, 13624, 11781, 9718, 8304, 17737, 12200, 20816, 7330, 7532, 6117, 4735, 4488, 2599, 3355, 7773, 22186, 27895, 9742, 726, 3330, 0, 2042],\n",
     "    [15056, 25994, 19589, 16743, 44198, 13078, 14967, 7552, 10422, 14935, 11891, 14002, 13225, 10671, 9475, 10633, 5084, 20315, 11866, 9802, 7682, 6471, 15720, 10674, 18908, 6204, 6000, 5066, 3039, 3721, 3496, 4772, 8614, 23805, 29519, 11614, 2749, 5313, 2042, 0],\n",
-    "]  # yapf: disable\n",
+    "    # fmt:on\n",
+    "]\n",
     "\n",
     "\n",
     "def main():\n",
     "    \"\"\"Entry point of the program.\"\"\"\n",
     "    num_nodes = len(DISTANCE_MATRIX)\n",
     "    all_nodes = range(num_nodes)\n",
-    "    print('Num nodes =', num_nodes)\n",
+    "    print(\"Num nodes =\", num_nodes)\n",
     "\n",
     "    # Model.\n",
     "    model = cp_model.CpModel()\n",
@@ -148,8 +151,8 @@
     "            if i == j:\n",
     "                continue\n",
     "\n",
-    "            lit = model.NewBoolVar('%i follows %i' % (j, i))\n",
-    "            arcs.append([i, j, lit])\n",
+    "            lit = model.NewBoolVar(\"%i follows %i\" % (j, i))\n",
+    "            arcs.append((i, j, lit))\n",
     "            arc_literals[i, j] = lit\n",
     "\n",
     "            obj_vars.append(lit)\n",
@@ -158,8 +161,7 @@
     "    model.AddCircuit(arcs)\n",
     "\n",
     "    # Minimize weighted sum of arcs. Because this s\n",
-    "    model.Minimize(\n",
-    "        sum(obj_vars[i] * obj_coeffs[i] for i in range(len(obj_vars))))\n",
+    "    model.Minimize(sum(obj_vars[i] * obj_coeffs[i] for i in range(len(obj_vars))))\n",
     "\n",
     "    # Solve and print out the solution.\n",
     "    solver = cp_model.CpSolver()\n",
@@ -171,7 +173,7 @@
     "    print(solver.ResponseStats())\n",
     "\n",
     "    current_node = 0\n",
-    "    str_route = '%i' % current_node\n",
+    "    str_route = \"%i\" % current_node\n",
     "    route_is_finished = False\n",
     "    route_distance = 0\n",
     "    while not route_is_finished:\n",
@@ -179,15 +181,15 @@
     "            if i == current_node:\n",
     "                continue\n",
     "            if solver.BooleanValue(arc_literals[current_node, i]):\n",
-    "                str_route += ' -> %i' % i\n",
+    "                str_route += \" -> %i\" % i\n",
     "                route_distance += DISTANCE_MATRIX[current_node][i]\n",
     "                current_node = i\n",
     "                if current_node == 0:\n",
     "                    route_is_finished = True\n",
     "                break\n",
     "\n",
-    "    print('Route:', str_route)\n",
-    "    print('Travelled distance:', route_distance)\n",
+    "    print(\"Route:\", str_route)\n",
+    "    print(\"Travelled distance:\", route_distance)\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/examples/vendor_scheduling_sat.ipynb b/examples/notebook/examples/vendor_scheduling_sat.ipynb
index c28f018447..4756aad908 100644
--- a/examples/notebook/examples/vendor_scheduling_sat.ipynb
+++ b/examples/notebook/examples/vendor_scheduling_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Solves a simple shift scheduling problem.\n"
    ]
   },
@@ -89,8 +90,15 @@
     "class SolutionPrinter(cp_model.CpSolverSolutionCallback):\n",
     "    \"\"\"Print intermediate solutions.\"\"\"\n",
     "\n",
-    "    def __init__(self, num_vendors, num_hours, possible_schedules,\n",
-    "                 selected_schedules, hours_stat, min_vendors):\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        num_vendors,\n",
+    "        num_hours,\n",
+    "        possible_schedules,\n",
+    "        selected_schedules,\n",
+    "        hours_stat,\n",
+    "        min_vendors,\n",
+    "    ):\n",
     "        cp_model.CpSolverSolutionCallback.__init__(self)\n",
     "        self.__solution_count = 0\n",
     "        self.__num_vendors = num_vendors\n",
@@ -103,17 +111,18 @@
     "    def on_solution_callback(self):\n",
     "        \"\"\"Called at each new solution.\"\"\"\n",
     "        self.__solution_count += 1\n",
-    "        print('Solution %i: ', self.__solution_count)\n",
-    "        print('  min vendors:', self.__min_vendors)\n",
+    "        print(\"Solution %i: \", self.__solution_count)\n",
+    "        print(\"  min vendors:\", self.__min_vendors)\n",
     "        for i in range(self.__num_vendors):\n",
     "            print(\n",
-    "                '  - vendor %i: ' % i, self.__possible_schedules[self.Value(\n",
-    "                    self.__selected_schedules[i])])\n",
+    "                \"  - vendor %i: \" % i,\n",
+    "                self.__possible_schedules[self.Value(self.__selected_schedules[i])],\n",
+    "            )\n",
     "        print()\n",
     "\n",
     "        for j in range(self.__num_hours):\n",
-    "            print('  - # workers on day%2i: ' % j, end=' ')\n",
-    "            print(self.Value(self.__hours_stat[j]), end=' ')\n",
+    "            print(\"  - # workers on day%2i: \" % j, end=\" \")\n",
+    "            print(self.Value(self.__hours_stat[j]), end=\" \")\n",
     "            print()\n",
     "        print()\n",
     "\n",
@@ -140,12 +149,14 @@
     "    # Last columns are :\n",
     "    #   index_of_the_schedule, sum of worked hours (per work type).\n",
     "    # The index is useful for branching.\n",
-    "    possible_schedules = [[1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 8],\n",
-    "                          [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 4],\n",
-    "                          [0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 2, 5],\n",
-    "                          [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 3, 4],\n",
-    "                          [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 4, 3],\n",
-    "                          [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0]]\n",
+    "    possible_schedules = [\n",
+    "        [1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 8],\n",
+    "        [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 4],\n",
+    "        [0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 2, 5],\n",
+    "        [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 3, 4],\n",
+    "        [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 4, 3],\n",
+    "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0],\n",
+    "    ]\n",
     "\n",
     "    num_possible_schedules = len(possible_schedules)\n",
     "    selected_schedules = []\n",
@@ -165,11 +176,10 @@
     "    for v in all_vendors:\n",
     "        tmp = []\n",
     "        for h in all_hours:\n",
-    "            x[v, h] = model.NewIntVar(0, num_work_types, 'x[%i,%i]' % (v, h))\n",
+    "            x[v, h] = model.NewIntVar(0, num_work_types, \"x[%i,%i]\" % (v, h))\n",
     "            tmp.append(x[v, h])\n",
-    "        selected_schedule = model.NewIntVar(0, num_possible_schedules - 1,\n",
-    "                                            's[%i]' % v)\n",
-    "        hours = model.NewIntVar(0, num_hours, 'h[%i]' % v)\n",
+    "        selected_schedule = model.NewIntVar(0, num_possible_schedules - 1, \"s[%i]\" % v)\n",
+    "        hours = model.NewIntVar(0, num_hours, \"h[%i]\" % v)\n",
     "        selected_schedules.append(selected_schedule)\n",
     "        vendors_stat.append(hours)\n",
     "        tmp.append(selected_schedule)\n",
@@ -181,7 +191,7 @@
     "    # Statistics and constraints for each hour\n",
     "    #\n",
     "    for h in all_hours:\n",
-    "        workers = model.NewIntVar(0, 1000, 'workers[%i]' % h)\n",
+    "        workers = model.NewIntVar(0, 1000, \"workers[%i]\" % h)\n",
     "        model.Add(workers == sum(x[v, h] for v in all_vendors))\n",
     "        hours_stat.append(workers)\n",
     "        model.Add(workers * max_traffic_per_vendor >= traffic[h])\n",
@@ -195,23 +205,27 @@
     "    # Solve model.\n",
     "    solver = cp_model.CpSolver()\n",
     "    solver.parameters.enumerate_all_solutions = True\n",
-    "    solution_printer = SolutionPrinter(num_vendors, num_hours,\n",
-    "                                       possible_schedules, selected_schedules,\n",
-    "                                       hours_stat, min_vendors)\n",
+    "    solution_printer = SolutionPrinter(\n",
+    "        num_vendors,\n",
+    "        num_hours,\n",
+    "        possible_schedules,\n",
+    "        selected_schedules,\n",
+    "        hours_stat,\n",
+    "        min_vendors,\n",
+    "    )\n",
     "    status = solver.Solve(model, solution_printer)\n",
-    "    print('Status = %s' % solver.StatusName(status))\n",
+    "    print(\"Status = %s\" % solver.StatusName(status))\n",
     "\n",
-    "    print('Statistics')\n",
-    "    print('  - conflicts : %i' % solver.NumConflicts())\n",
-    "    print('  - branches  : %i' % solver.NumBranches())\n",
-    "    print('  - wall time : %f s' % solver.WallTime())\n",
-    "    print('  - number of solutions found: %i' %\n",
-    "          solution_printer.solution_count())\n",
+    "    print(\"Statistics\")\n",
+    "    print(\"  - conflicts : %i\" % solver.NumConflicts())\n",
+    "    print(\"  - branches  : %i\" % solver.NumBranches())\n",
+    "    print(\"  - wall time : %f s\" % solver.WallTime())\n",
+    "    print(\"  - number of solutions found: %i\" % solution_printer.solution_count())\n",
     "\n",
     "\n",
     "def main(argv: Sequence[str]) -> None:\n",
     "    if len(argv) > 1:\n",
-    "        raise app.UsageError('Too many command-line arguments.')\n",
+    "        raise app.UsageError(\"Too many command-line arguments.\")\n",
     "    vendor_scheduling_sat()\n",
     "\n",
     "\n",
diff --git a/examples/notebook/examples/wedding_optimal_chart_sat.ipynb b/examples/notebook/examples/wedding_optimal_chart_sat.ipynb
index fa4db04dd9..beb62e3d07 100644
--- a/examples/notebook/examples/wedding_optimal_chart_sat.ipynb
+++ b/examples/notebook/examples/wedding_optimal_chart_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Finding an optimal wedding seating chart.\n",
     "\n",
     "From\n",
@@ -124,9 +125,10 @@
     "    def on_solution_callback(self):\n",
     "        current_time = time.time()\n",
     "        objective = self.ObjectiveValue()\n",
-    "        print(\"Solution %i, time = %f s, objective = %i\" %\n",
-    "              (self.__solution_count, current_time - self.__start_time,\n",
-    "               objective))\n",
+    "        print(\n",
+    "            \"Solution %i, time = %f s, objective = %i\"\n",
+    "            % (self.__solution_count, current_time - self.__start_time, objective)\n",
+    "        )\n",
     "        self.__solution_count += 1\n",
     "\n",
     "        for t in range(self.__num_tables):\n",
@@ -153,38 +155,52 @@
     "\n",
     "    # Connection matrix: who knows who, and how strong\n",
     "    # is the relation\n",
-    "    connections = [[1, 50, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
-    "                   [50, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
-    "                   [1, 1, 1, 50, 1, 1, 1, 1, 10, 0, 0, 0, 0, 0, 0, 0, 0],\n",
-    "                   [1, 1, 50, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
-    "                   [1, 1, 1, 1, 1, 50, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
-    "                   [1, 1, 1, 1, 50, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
-    "                   [1, 1, 1, 1, 1, 1, 1, 50, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
-    "                   [1, 1, 1, 1, 1, 1, 50, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
-    "                   [1, 1, 10, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
-    "                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 50, 1, 1, 1, 1, 1, 1],\n",
-    "                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 50, 1, 1, 1, 1, 1, 1, 1],\n",
-    "                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n",
-    "                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n",
-    "                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n",
-    "                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n",
-    "                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n",
-    "                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]]\n",
+    "    connections = [\n",
+    "        [1, 50, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+    "        [50, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+    "        [1, 1, 1, 50, 1, 1, 1, 1, 10, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+    "        [1, 1, 50, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+    "        [1, 1, 1, 1, 1, 50, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+    "        [1, 1, 1, 1, 50, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+    "        [1, 1, 1, 1, 1, 1, 1, 50, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+    "        [1, 1, 1, 1, 1, 1, 50, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+    "        [1, 1, 10, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+    "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 50, 1, 1, 1, 1, 1, 1],\n",
+    "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 50, 1, 1, 1, 1, 1, 1, 1],\n",
+    "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+    "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+    "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+    "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+    "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+    "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+    "    ]\n",
     "\n",
     "    # Names of the guests. B: Bride side, G: Groom side\n",
     "    names = [\n",
-    "        \"Deb (B)\", \"John (B)\", \"Martha (B)\", \"Travis (B)\", \"Allan (B)\",\n",
-    "        \"Lois (B)\", \"Jayne (B)\", \"Brad (B)\", \"Abby (B)\", \"Mary Helen (G)\",\n",
-    "        \"Lee (G)\", \"Annika (G)\", \"Carl (G)\", \"Colin (G)\", \"Shirley (G)\",\n",
-    "        \"DeAnn (G)\", \"Lori (G)\"\n",
+    "        \"Deb (B)\",\n",
+    "        \"John (B)\",\n",
+    "        \"Martha (B)\",\n",
+    "        \"Travis (B)\",\n",
+    "        \"Allan (B)\",\n",
+    "        \"Lois (B)\",\n",
+    "        \"Jayne (B)\",\n",
+    "        \"Brad (B)\",\n",
+    "        \"Abby (B)\",\n",
+    "        \"Mary Helen (G)\",\n",
+    "        \"Lee (G)\",\n",
+    "        \"Annika (G)\",\n",
+    "        \"Carl (G)\",\n",
+    "        \"Colin (G)\",\n",
+    "        \"Shirley (G)\",\n",
+    "        \"DeAnn (G)\",\n",
+    "        \"Lori (G)\",\n",
     "    ]\n",
     "    return num_tables, table_capacity, min_known_neighbors, connections, names\n",
     "\n",
     "\n",
     "def solve_with_discrete_model():\n",
     "    \"\"\"Discrete approach.\"\"\"\n",
-    "    num_tables, table_capacity, min_known_neighbors, connections, names = build_data(\n",
-    "    )\n",
+    "    num_tables, table_capacity, min_known_neighbors, connections, names = build_data()\n",
     "\n",
     "    num_guests = len(connections)\n",
     "\n",
@@ -200,28 +216,32 @@
     "    seats = {}\n",
     "    for t in all_tables:\n",
     "        for g in all_guests:\n",
-    "            seats[(t,\n",
-    "                   g)] = model.NewBoolVar(\"guest %i seats on table %i\" % (g, t))\n",
+    "            seats[(t, g)] = model.NewBoolVar(\"guest %i seats on table %i\" % (g, t))\n",
     "\n",
     "    colocated = {}\n",
     "    for g1 in range(num_guests - 1):\n",
     "        for g2 in range(g1 + 1, num_guests):\n",
     "            colocated[(g1, g2)] = model.NewBoolVar(\n",
-    "                \"guest %i seats with guest %i\" % (g1, g2))\n",
+    "                \"guest %i seats with guest %i\" % (g1, g2)\n",
+    "            )\n",
     "\n",
     "    same_table = {}\n",
     "    for g1 in range(num_guests - 1):\n",
     "        for g2 in range(g1 + 1, num_guests):\n",
     "            for t in all_tables:\n",
     "                same_table[(g1, g2, t)] = model.NewBoolVar(\n",
-    "                    \"guest %i seats with guest %i on table %i\" % (g1, g2, t))\n",
+    "                    \"guest %i seats with guest %i on table %i\" % (g1, g2, t)\n",
+    "                )\n",
     "\n",
     "    # Objective\n",
     "    model.Maximize(\n",
-    "        sum(connections[g1][g2] * colocated[g1, g2]\n",
+    "        sum(\n",
+    "            connections[g1][g2] * colocated[g1, g2]\n",
     "            for g1 in range(num_guests - 1)\n",
     "            for g2 in range(g1 + 1, num_guests)\n",
-    "            if connections[g1][g2] > 0))\n",
+    "            if connections[g1][g2] > 0\n",
+    "        )\n",
+    "    )\n",
     "\n",
     "    #\n",
     "    # Constraints\n",
@@ -240,29 +260,38 @@
     "        for g2 in range(g1 + 1, num_guests):\n",
     "            for t in all_tables:\n",
     "                # Link same_table and seats.\n",
-    "                model.AddBoolOr([\n",
-    "                    seats[(t, g1)].Not(), seats[(t, g2)].Not(),\n",
-    "                    same_table[(g1, g2, t)]\n",
-    "                ])\n",
+    "                model.AddBoolOr(\n",
+    "                    [\n",
+    "                        seats[(t, g1)].Not(),\n",
+    "                        seats[(t, g2)].Not(),\n",
+    "                        same_table[(g1, g2, t)],\n",
+    "                    ]\n",
+    "                )\n",
     "                model.AddImplication(same_table[(g1, g2, t)], seats[(t, g1)])\n",
     "                model.AddImplication(same_table[(g1, g2, t)], seats[(t, g2)])\n",
     "\n",
     "            # Link colocated and same_table.\n",
     "            model.Add(\n",
-    "                sum(same_table[(g1, g2, t)] for t in all_tables) == colocated[(\n",
-    "                    g1, g2)])\n",
+    "                sum(same_table[(g1, g2, t)] for t in all_tables) == colocated[(g1, g2)]\n",
+    "            )\n",
     "\n",
     "    # Min known neighbors rule.\n",
     "    for g in all_guests:\n",
     "        model.Add(\n",
-    "            sum(same_table[(g, g2, t)]\n",
+    "            sum(\n",
+    "                same_table[(g, g2, t)]\n",
     "                for g2 in range(g + 1, num_guests)\n",
     "                for t in all_tables\n",
-    "                if connections[g][g2] > 0) +\n",
-    "            sum(same_table[(g1, g, t)]\n",
+    "                if connections[g][g2] > 0\n",
+    "            )\n",
+    "            + sum(\n",
+    "                same_table[(g1, g, t)]\n",
     "                for g1 in range(g)\n",
     "                for t in all_tables\n",
-    "                if connections[g1][g] > 0) >= min_known_neighbors)\n",
+    "                if connections[g1][g] > 0\n",
+    "            )\n",
+    "            >= min_known_neighbors\n",
+    "        )\n",
     "\n",
     "    # Symmetry breaking. First guest seats on the first table.\n",
     "    model.Add(seats[(0, 0)] == 1)\n",
diff --git a/examples/notebook/examples/weighted_latency_problem_sat.ipynb b/examples/notebook/examples/weighted_latency_problem_sat.ipynb
index 0c131d2712..dfb20664d1 100644
--- a/examples/notebook/examples/weighted_latency_problem_sat.ipynb
+++ b/examples/notebook/examples/weighted_latency_problem_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Solve a random Weighted Latency problem with the CP-SAT solver.\n"
    ]
   },
@@ -87,16 +88,16 @@
     "from google.protobuf import text_format\n",
     "from ortools.sat.python import cp_model\n",
     "\n",
-    "_NUM_NODES = flags.DEFINE_integer('num_nodes', 12, 'Number of nodes to visit.')\n",
-    "_GRID_SIZE = flags.DEFINE_integer('grid_size', 20,\n",
-    "                                  'Size of the grid where nodes are.')\n",
-    "_PROFIT_RANGE = flags.DEFINE_integer('profit_range', 50, 'Range of profit.')\n",
-    "_SEED = flags.DEFINE_integer('seed', 0, 'Random seed.')\n",
-    "_PARAMS = flags.DEFINE_string('params',\n",
-    "                              'num_search_workers:16, max_time_in_seconds:5',\n",
-    "                              'Sat solver parameters.')\n",
+    "_NUM_NODES = flags.DEFINE_integer(\"num_nodes\", 12, \"Number of nodes to visit.\")\n",
+    "_GRID_SIZE = flags.DEFINE_integer(\"grid_size\", 20, \"Size of the grid where nodes are.\")\n",
+    "_PROFIT_RANGE = flags.DEFINE_integer(\"profit_range\", 50, \"Range of profit.\")\n",
+    "_SEED = flags.DEFINE_integer(\"seed\", 0, \"Random seed.\")\n",
+    "_PARAMS = flags.DEFINE_string(\n",
+    "    \"params\", \"num_search_workers:16, max_time_in_seconds:5\", \"Sat solver parameters.\"\n",
+    ")\n",
     "_PROTO_FILE = flags.DEFINE_string(\n",
-    "    'proto_file', '', 'If not empty, output the proto to this file.')\n",
+    "    \"proto_file\", \"\", \"If not empty, output the proto to this file.\"\n",
+    ")\n",
     "\n",
     "\n",
     "def build_model():\n",
@@ -126,10 +127,7 @@
     "\n",
     "    # because of the manhattan distance, the sum of distances is bounded by this.\n",
     "    horizon = _GRID_SIZE.value * 2 * _NUM_NODES.value\n",
-    "    times = [\n",
-    "        model.NewIntVar(0, horizon, f'x_{i}')\n",
-    "        for i in range(_NUM_NODES.value + 1)\n",
-    "    ]\n",
+    "    times = [model.NewIntVar(0, horizon, f\"x_{i}\") for i in range(_NUM_NODES.value + 1)]\n",
     "\n",
     "    # Node 0 is the start node.\n",
     "    model.Add(times[0] == 0)\n",
@@ -142,7 +140,7 @@
     "                continue\n",
     "            # We use a manhattan distance between nodes.\n",
     "            distance = abs(x[i] - x[j]) + abs(y[i] - y[j])\n",
-    "            lit = model.NewBoolVar(f'{i}_to_{j}')\n",
+    "            lit = model.NewBoolVar(f\"{i}_to_{j}\")\n",
     "            arcs.append((i, j, lit))\n",
     "\n",
     "            # Add transitions between nodes.\n",
@@ -169,7 +167,7 @@
     "\n",
     "def main(argv: Sequence[str]) -> None:\n",
     "    if len(argv) > 1:\n",
-    "        raise app.UsageError('Too many command-line arguments.')\n",
+    "        raise app.UsageError(\"Too many command-line arguments.\")\n",
     "\n",
     "    x, y, profits = build_model()\n",
     "    solve_with_cp_sat(x, y, profits)\n",
diff --git a/examples/notebook/examples/zebra_sat.ipynb b/examples/notebook/examples/zebra_sat.ipynb
index 16a3460c27..e727d0f2a6 100644
--- a/examples/notebook/examples/zebra_sat.ipynb
+++ b/examples/notebook/examples/zebra_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "This is the zebra problem as invented by Lewis Caroll.\n",
     "\n",
     "There are five houses.\n",
@@ -112,42 +113,41 @@
     "    # Create the model.\n",
     "    model = cp_model.CpModel()\n",
     "\n",
-    "    red = model.NewIntVar(1, 5, 'red')\n",
-    "    green = model.NewIntVar(1, 5, 'green')\n",
-    "    yellow = model.NewIntVar(1, 5, 'yellow')\n",
-    "    blue = model.NewIntVar(1, 5, 'blue')\n",
-    "    ivory = model.NewIntVar(1, 5, 'ivory')\n",
+    "    red = model.NewIntVar(1, 5, \"red\")\n",
+    "    green = model.NewIntVar(1, 5, \"green\")\n",
+    "    yellow = model.NewIntVar(1, 5, \"yellow\")\n",
+    "    blue = model.NewIntVar(1, 5, \"blue\")\n",
+    "    ivory = model.NewIntVar(1, 5, \"ivory\")\n",
     "\n",
-    "    englishman = model.NewIntVar(1, 5, 'englishman')\n",
-    "    spaniard = model.NewIntVar(1, 5, 'spaniard')\n",
-    "    japanese = model.NewIntVar(1, 5, 'japanese')\n",
-    "    ukrainian = model.NewIntVar(1, 5, 'ukrainian')\n",
-    "    norwegian = model.NewIntVar(1, 5, 'norwegian')\n",
+    "    englishman = model.NewIntVar(1, 5, \"englishman\")\n",
+    "    spaniard = model.NewIntVar(1, 5, \"spaniard\")\n",
+    "    japanese = model.NewIntVar(1, 5, \"japanese\")\n",
+    "    ukrainian = model.NewIntVar(1, 5, \"ukrainian\")\n",
+    "    norwegian = model.NewIntVar(1, 5, \"norwegian\")\n",
     "\n",
-    "    dog = model.NewIntVar(1, 5, 'dog')\n",
-    "    snails = model.NewIntVar(1, 5, 'snails')\n",
-    "    fox = model.NewIntVar(1, 5, 'fox')\n",
-    "    zebra = model.NewIntVar(1, 5, 'zebra')\n",
-    "    horse = model.NewIntVar(1, 5, 'horse')\n",
+    "    dog = model.NewIntVar(1, 5, \"dog\")\n",
+    "    snails = model.NewIntVar(1, 5, \"snails\")\n",
+    "    fox = model.NewIntVar(1, 5, \"fox\")\n",
+    "    zebra = model.NewIntVar(1, 5, \"zebra\")\n",
+    "    horse = model.NewIntVar(1, 5, \"horse\")\n",
     "\n",
-    "    tea = model.NewIntVar(1, 5, 'tea')\n",
-    "    coffee = model.NewIntVar(1, 5, 'coffee')\n",
-    "    water = model.NewIntVar(1, 5, 'water')\n",
-    "    milk = model.NewIntVar(1, 5, 'milk')\n",
-    "    fruit_juice = model.NewIntVar(1, 5, 'fruit juice')\n",
+    "    tea = model.NewIntVar(1, 5, \"tea\")\n",
+    "    coffee = model.NewIntVar(1, 5, \"coffee\")\n",
+    "    water = model.NewIntVar(1, 5, \"water\")\n",
+    "    milk = model.NewIntVar(1, 5, \"milk\")\n",
+    "    fruit_juice = model.NewIntVar(1, 5, \"fruit juice\")\n",
     "\n",
-    "    old_gold = model.NewIntVar(1, 5, 'old gold')\n",
-    "    kools = model.NewIntVar(1, 5, 'kools')\n",
-    "    chesterfields = model.NewIntVar(1, 5, 'chesterfields')\n",
-    "    lucky_strike = model.NewIntVar(1, 5, 'lucky strike')\n",
-    "    parliaments = model.NewIntVar(1, 5, 'parliaments')\n",
+    "    old_gold = model.NewIntVar(1, 5, \"old gold\")\n",
+    "    kools = model.NewIntVar(1, 5, \"kools\")\n",
+    "    chesterfields = model.NewIntVar(1, 5, \"chesterfields\")\n",
+    "    lucky_strike = model.NewIntVar(1, 5, \"lucky strike\")\n",
+    "    parliaments = model.NewIntVar(1, 5, \"parliaments\")\n",
     "\n",
     "    model.AddAllDifferent(red, green, yellow, blue, ivory)\n",
     "    model.AddAllDifferent(englishman, spaniard, japanese, ukrainian, norwegian)\n",
     "    model.AddAllDifferent(dog, snails, fox, zebra, horse)\n",
     "    model.AddAllDifferent(tea, coffee, water, milk, fruit_juice)\n",
-    "    model.AddAllDifferent(parliaments, kools, chesterfields, lucky_strike,\n",
-    "                          old_gold)\n",
+    "    model.AddAllDifferent(parliaments, kools, chesterfields, lucky_strike, old_gold)\n",
     "\n",
     "    model.Add(englishman == red)\n",
     "    model.Add(spaniard == dog)\n",
@@ -159,18 +159,18 @@
     "    model.Add(milk == 3)\n",
     "    model.Add(norwegian == 1)\n",
     "\n",
-    "    diff_fox_chesterfields = model.NewIntVar(-4, 4, 'diff_fox_chesterfields')\n",
+    "    diff_fox_chesterfields = model.NewIntVar(-4, 4, \"diff_fox_chesterfields\")\n",
     "    model.Add(diff_fox_chesterfields == fox - chesterfields)\n",
     "    model.AddAbsEquality(1, diff_fox_chesterfields)\n",
     "\n",
-    "    diff_horse_kools = model.NewIntVar(-4, 4, 'diff_horse_kools')\n",
+    "    diff_horse_kools = model.NewIntVar(-4, 4, \"diff_horse_kools\")\n",
     "    model.Add(diff_horse_kools == horse - kools)\n",
     "    model.AddAbsEquality(1, diff_horse_kools)\n",
     "\n",
     "    model.Add(lucky_strike == fruit_juice)\n",
     "    model.Add(japanese == parliaments)\n",
     "\n",
-    "    diff_norwegian_blue = model.NewIntVar(-4, 4, 'diff_norwegian_blue')\n",
+    "    diff_norwegian_blue = model.NewIntVar(-4, 4, \"diff_norwegian_blue\")\n",
     "    model.Add(diff_norwegian_blue == norwegian - blue)\n",
     "    model.AddAbsEquality(1, diff_norwegian_blue)\n",
     "\n",
@@ -180,16 +180,12 @@
     "\n",
     "    if status == cp_model.OPTIMAL:\n",
     "        people = [englishman, spaniard, japanese, ukrainian, norwegian]\n",
-    "        water_drinker = [\n",
-    "            p for p in people if solver.Value(p) == solver.Value(water)\n",
-    "        ][0]\n",
-    "        zebra_owner = [\n",
-    "            p for p in people if solver.Value(p) == solver.Value(zebra)\n",
-    "        ][0]\n",
-    "        print('The', water_drinker.Name(), 'drinks water.')\n",
-    "        print('The', zebra_owner.Name(), 'owns the zebra.')\n",
+    "        water_drinker = [p for p in people if solver.Value(p) == solver.Value(water)][0]\n",
+    "        zebra_owner = [p for p in people if solver.Value(p) == solver.Value(zebra)][0]\n",
+    "        print(\"The\", water_drinker.Name(), \"drinks water.\")\n",
+    "        print(\"The\", zebra_owner.Name(), \"owns the zebra.\")\n",
     "    else:\n",
-    "        print('No solutions to the zebra problem, this is unusual!')\n",
+    "        print(\"No solutions to the zebra problem, this is unusual!\")\n",
     "\n",
     "\n",
     "solve_zebra()\n",
diff --git a/examples/notebook/graph/assignment_linear_sum_assignment.ipynb b/examples/notebook/graph/assignment_linear_sum_assignment.ipynb
index 71c3ec7362..65308d8038 100644
--- a/examples/notebook/graph/assignment_linear_sum_assignment.ipynb
+++ b/examples/notebook/graph/assignment_linear_sum_assignment.ipynb
@@ -92,17 +92,20 @@
     "    \"\"\"Linear Sum Assignment example.\"\"\"\n",
     "    assignment = linear_sum_assignment.SimpleLinearSumAssignment()\n",
     "\n",
-    "    costs = np.array([\n",
-    "        [90, 76, 75, 70],\n",
-    "        [35, 85, 55, 65],\n",
-    "        [125, 95, 90, 105],\n",
-    "        [45, 110, 95, 115],\n",
-    "    ])\n",
+    "    costs = np.array(\n",
+    "        [\n",
+    "            [90, 76, 75, 70],\n",
+    "            [35, 85, 55, 65],\n",
+    "            [125, 95, 90, 105],\n",
+    "            [45, 110, 95, 115],\n",
+    "        ]\n",
+    "    )\n",
     "\n",
     "    # Let's transform this into 3 parallel vectors (start_nodes, end_nodes,\n",
     "    # arc_costs)\n",
     "    end_nodes_unraveled, start_nodes_unraveled = np.meshgrid(\n",
-    "        np.arange(costs.shape[1]), np.arange(costs.shape[0]))\n",
+    "        np.arange(costs.shape[1]), np.arange(costs.shape[0])\n",
+    "    )\n",
     "    start_nodes = start_nodes_unraveled.ravel()\n",
     "    end_nodes = end_nodes_unraveled.ravel()\n",
     "    arc_costs = costs.ravel()\n",
@@ -112,15 +115,16 @@
     "    status = assignment.solve()\n",
     "\n",
     "    if status == assignment.OPTIMAL:\n",
-    "        print(f'Total cost = {assignment.optimal_cost()}\\n')\n",
+    "        print(f\"Total cost = {assignment.optimal_cost()}\\n\")\n",
     "        for i in range(0, assignment.num_nodes()):\n",
-    "            print(f'Worker {i} assigned to task {assignment.right_mate(i)}.' +\n",
-    "                  f'  Cost = {assignment.assignment_cost(i)}')\n",
+    "            print(\n",
+    "                f\"Worker {i} assigned to task {assignment.right_mate(i)}.\"\n",
+    "                + f\"  Cost = {assignment.assignment_cost(i)}\"\n",
+    "            )\n",
     "    elif status == assignment.INFEASIBLE:\n",
-    "        print('No assignment is possible.')\n",
+    "        print(\"No assignment is possible.\")\n",
     "    elif status == assignment.POSSIBLE_OVERFLOW:\n",
-    "        print(\n",
-    "            'Some input costs are too large and may cause an integer overflow.')\n",
+    "        print(\"Some input costs are too large and may cause an integer overflow.\")\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/graph/assignment_min_flow.ipynb b/examples/notebook/graph/assignment_min_flow.ipynb
index 337a44f023..31d69648b9 100644
--- a/examples/notebook/graph/assignment_min_flow.ipynb
+++ b/examples/notebook/graph/assignment_min_flow.ipynb
@@ -92,18 +92,20 @@
     "    smcf = min_cost_flow.SimpleMinCostFlow()\n",
     "\n",
     "    # Define the directed graph for the flow.\n",
-    "    start_nodes = [0, 0, 0, 0] + [\n",
-    "        1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4\n",
-    "    ] + [5, 6, 7, 8]\n",
-    "    end_nodes = [1, 2, 3, 4] + [5, 6, 7, 8, 5, 6, 7, 8, 5, 6, 7, 8, 5, 6, 7, 8\n",
-    "                               ] + [9, 9, 9, 9]\n",
-    "    capacities = [1, 1, 1, 1] + [\n",
-    "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1\n",
-    "    ] + [1, 1, 1, 1]\n",
+    "    start_nodes = (\n",
+    "        [0, 0, 0, 0] + [1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4] + [5, 6, 7, 8]\n",
+    "    )\n",
+    "    end_nodes = (\n",
+    "        [1, 2, 3, 4] + [5, 6, 7, 8, 5, 6, 7, 8, 5, 6, 7, 8, 5, 6, 7, 8] + [9, 9, 9, 9]\n",
+    "    )\n",
+    "    capacities = (\n",
+    "        [1, 1, 1, 1] + [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] + [1, 1, 1, 1]\n",
+    "    )\n",
     "    costs = (\n",
-    "        [0, 0, 0, 0] +\n",
-    "        [90, 76, 75, 70, 35, 85, 55, 65, 125, 95, 90, 105, 45, 110, 95, 115] +\n",
-    "        [0, 0, 0, 0])\n",
+    "        [0, 0, 0, 0]\n",
+    "        + [90, 76, 75, 70, 35, 85, 55, 65, 125, 95, 90, 105, 45, 110, 95, 115]\n",
+    "        + [0, 0, 0, 0]\n",
+    "    )\n",
     "\n",
     "    source = 0\n",
     "    sink = 9\n",
@@ -112,8 +114,9 @@
     "\n",
     "    # Add each arc.\n",
     "    for i in range(len(start_nodes)):\n",
-    "        smcf.add_arc_with_capacity_and_unit_cost(start_nodes[i], end_nodes[i],\n",
-    "                                                 capacities[i], costs[i])\n",
+    "        smcf.add_arc_with_capacity_and_unit_cost(\n",
+    "            start_nodes[i], end_nodes[i], capacities[i], costs[i]\n",
+    "        )\n",
     "    # Add node supplies.\n",
     "    for i in range(len(supplies)):\n",
     "        smcf.set_node_supply(i, supplies[i])\n",
@@ -122,20 +125,21 @@
     "    status = smcf.solve()\n",
     "\n",
     "    if status == smcf.OPTIMAL:\n",
-    "        print('Total cost = ', smcf.optimal_cost())\n",
+    "        print(\"Total cost = \", smcf.optimal_cost())\n",
     "        print()\n",
     "        for arc in range(smcf.num_arcs()):\n",
     "            # Can ignore arcs leading out of source or into sink.\n",
     "            if smcf.tail(arc) != source and smcf.head(arc) != sink:\n",
-    "\n",
     "                # Arcs in the solution have a flow value of 1. Their start and end nodes\n",
     "                # give an assignment of worker to task.\n",
     "                if smcf.flow(arc) > 0:\n",
-    "                    print('Worker %d assigned to task %d.  Cost = %d' %\n",
-    "                          (smcf.tail(arc), smcf.head(arc), smcf.unit_cost(arc)))\n",
+    "                    print(\n",
+    "                        \"Worker %d assigned to task %d.  Cost = %d\"\n",
+    "                        % (smcf.tail(arc), smcf.head(arc), smcf.unit_cost(arc))\n",
+    "                    )\n",
     "    else:\n",
-    "        print('There was an issue with the min cost flow input.')\n",
-    "        print(f'Status: {status}')\n",
+    "        print(\"There was an issue with the min cost flow input.\")\n",
+    "        print(f\"Status: {status}\")\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/graph/balance_min_flow.ipynb b/examples/notebook/graph/balance_min_flow.ipynb
index 8ef3b2fbef..6905a62cfd 100644
--- a/examples/notebook/graph/balance_min_flow.ipynb
+++ b/examples/notebook/graph/balance_min_flow.ipynb
@@ -94,20 +94,42 @@
     "    team_a = [1, 3, 5]\n",
     "    team_b = [2, 4, 6]\n",
     "\n",
-    "    start_nodes = ([0, 0] + [11, 11, 11] + [12, 12, 12] + [\n",
-    "        1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6\n",
-    "    ] + [7, 8, 9, 10])\n",
-    "    end_nodes = ([11, 12] + team_a + team_b + [\n",
-    "        7, 8, 9, 10, 7, 8, 9, 10, 7, 8, 9, 10, 7, 8, 9, 10, 7, 8, 9, 10, 7, 8,\n",
-    "        9, 10\n",
-    "    ] + [13, 13, 13, 13])\n",
-    "    capacities = ([2, 2] + [1, 1, 1] + [1, 1, 1] + [\n",
-    "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1\n",
-    "    ] + [1, 1, 1, 1])\n",
-    "    costs = ([0, 0] + [0, 0, 0] + [0, 0, 0] + [\n",
-    "        90, 76, 75, 70, 35, 85, 55, 65, 125, 95, 90, 105, 45, 110, 95, 115, 60,\n",
-    "        105, 80, 75, 45, 65, 110, 95\n",
-    "    ] + [0, 0, 0, 0])\n",
+    "    start_nodes = (\n",
+    "        # fmt: off\n",
+    "      [0, 0]\n",
+    "      + [11, 11, 11]\n",
+    "      + [12, 12, 12]\n",
+    "      + [1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6]\n",
+    "      + [7, 8, 9, 10]\n",
+    "        # fmt: on\n",
+    "    )\n",
+    "    end_nodes = (\n",
+    "        # fmt: off\n",
+    "      [11, 12]\n",
+    "      + team_a\n",
+    "      + team_b\n",
+    "      + [7, 8, 9, 10, 7, 8, 9, 10, 7, 8, 9, 10, 7, 8, 9, 10, 7, 8, 9, 10, 7, 8, 9, 10]\n",
+    "      + [13, 13, 13, 13]\n",
+    "        # fmt: on\n",
+    "    )\n",
+    "    capacities = (\n",
+    "        # fmt: off\n",
+    "      [2, 2]\n",
+    "      + [1, 1, 1]\n",
+    "      + [1, 1, 1]\n",
+    "      + [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
+    "      + [1, 1, 1, 1]\n",
+    "        # fmt: on\n",
+    "    )\n",
+    "    costs = (\n",
+    "        # fmt: off\n",
+    "      [0, 0]\n",
+    "      + [0, 0, 0]\n",
+    "      + [0, 0, 0]\n",
+    "      + [90, 76, 75, 70, 35, 85, 55, 65, 125, 95, 90, 105, 45, 110, 95, 115, 60, 105, 80, 75, 45, 65, 110, 95]\n",
+    "      + [0, 0, 0, 0]\n",
+    "        # fmt: on\n",
+    "    )\n",
     "\n",
     "    source = 0\n",
     "    sink = 13\n",
@@ -117,8 +139,9 @@
     "\n",
     "    # Add each arc.\n",
     "    for i in range(0, len(start_nodes)):\n",
-    "        smcf.add_arc_with_capacity_and_unit_cost(start_nodes[i], end_nodes[i],\n",
-    "                                                 capacities[i], costs[i])\n",
+    "        smcf.add_arc_with_capacity_and_unit_cost(\n",
+    "            start_nodes[i], end_nodes[i], capacities[i], costs[i]\n",
+    "        )\n",
     "\n",
     "    # Add node supplies.\n",
     "    for i in range(0, len(supplies)):\n",
@@ -128,21 +151,26 @@
     "    status = smcf.solve()\n",
     "\n",
     "    if status == smcf.OPTIMAL:\n",
-    "        print('Total cost = ', smcf.optimal_cost())\n",
+    "        print(\"Total cost = \", smcf.optimal_cost())\n",
     "        print()\n",
     "        for arc in range(smcf.num_arcs()):\n",
     "            # Can ignore arcs leading out of source or intermediate, or into sink.\n",
-    "            if (smcf.tail(arc) != source and smcf.tail(arc) != 11 and\n",
-    "                    smcf.tail(arc) != 12 and smcf.head(arc) != sink):\n",
-    "\n",
+    "            if (\n",
+    "                smcf.tail(arc) != source\n",
+    "                and smcf.tail(arc) != 11\n",
+    "                and smcf.tail(arc) != 12\n",
+    "                and smcf.head(arc) != sink\n",
+    "            ):\n",
     "                # Arcs in the solution will have a flow value of 1.\n",
     "                # There start and end nodes give an assignment of worker to task.\n",
     "                if smcf.flow(arc) > 0:\n",
-    "                    print('Worker %d assigned to task %d.  Cost = %d' %\n",
-    "                          (smcf.tail(arc), smcf.head(arc), smcf.unit_cost(arc)))\n",
+    "                    print(\n",
+    "                        \"Worker %d assigned to task %d.  Cost = %d\"\n",
+    "                        % (smcf.tail(arc), smcf.head(arc), smcf.unit_cost(arc))\n",
+    "                    )\n",
     "    else:\n",
-    "        print('There was an issue with the min cost flow input.')\n",
-    "        print(f'Status: {status}')\n",
+    "        print(\"There was an issue with the min cost flow input.\")\n",
+    "        print(f\"Status: {status}\")\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/graph/simple_max_flow_program.ipynb b/examples/notebook/graph/simple_max_flow_program.ipynb
index b93b87c792..c1b43b2721 100644
--- a/examples/notebook/graph/simple_max_flow_program.ipynb
+++ b/examples/notebook/graph/simple_max_flow_program.ipynb
@@ -108,17 +108,17 @@
     "    status = smf.solve(0, 4)\n",
     "\n",
     "    if status != smf.OPTIMAL:\n",
-    "        print('There was an issue with the max flow input.')\n",
-    "        print(f'Status: {status}')\n",
+    "        print(\"There was an issue with the max flow input.\")\n",
+    "        print(f\"Status: {status}\")\n",
     "        exit(1)\n",
-    "    print('Max flow:', smf.optimal_flow())\n",
-    "    print('')\n",
-    "    print(' Arc    Flow / Capacity')\n",
+    "    print(\"Max flow:\", smf.optimal_flow())\n",
+    "    print(\"\")\n",
+    "    print(\" Arc    Flow / Capacity\")\n",
     "    solution_flows = smf.flows(all_arcs)\n",
     "    for arc, flow, capacity in zip(all_arcs, solution_flows, capacities):\n",
-    "        print(f'{smf.tail(arc)} / {smf.head(arc)}   {flow:3}  / {capacity:3}')\n",
-    "    print('Source side min-cut:', smf.get_source_side_min_cut())\n",
-    "    print('Sink side min-cut:', smf.get_sink_side_min_cut())\n",
+    "        print(f\"{smf.tail(arc)} / {smf.head(arc)}   {flow:3}  / {capacity:3}\")\n",
+    "    print(\"Source side min-cut:\", smf.get_source_side_min_cut())\n",
+    "    print(\"Sink side min-cut:\", smf.get_sink_side_min_cut())\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/graph/simple_min_cost_flow_program.ipynb b/examples/notebook/graph/simple_min_cost_flow_program.ipynb
index 86cbbaa613..a91f6a7629 100644
--- a/examples/notebook/graph/simple_min_cost_flow_program.ipynb
+++ b/examples/notebook/graph/simple_min_cost_flow_program.ipynb
@@ -106,7 +106,8 @@
     "\n",
     "    # Add arcs, capacities and costs in bulk using numpy.\n",
     "    all_arcs = smcf.add_arcs_with_capacity_and_unit_cost(\n",
-    "        start_nodes, end_nodes, capacities, unit_costs)\n",
+    "        start_nodes, end_nodes, capacities, unit_costs\n",
+    "    )\n",
     "\n",
     "    # Add supply for each nodes.\n",
     "    smcf.set_nodes_supplies(np.arange(0, len(supplies)), supplies)\n",
@@ -115,17 +116,17 @@
     "    status = smcf.solve()\n",
     "\n",
     "    if status != smcf.OPTIMAL:\n",
-    "        print('There was an issue with the min cost flow input.')\n",
-    "        print(f'Status: {status}')\n",
+    "        print(\"There was an issue with the min cost flow input.\")\n",
+    "        print(f\"Status: {status}\")\n",
     "        exit(1)\n",
-    "    print(f'Minimum cost: {smcf.optimal_cost()}')\n",
-    "    print('')\n",
-    "    print(' Arc    Flow / Capacity Cost')\n",
+    "    print(f\"Minimum cost: {smcf.optimal_cost()}\")\n",
+    "    print(\"\")\n",
+    "    print(\" Arc    Flow / Capacity Cost\")\n",
     "    solution_flows = smcf.flows(all_arcs)\n",
     "    costs = solution_flows * unit_costs\n",
     "    for arc, flow, cost in zip(all_arcs, solution_flows, costs):\n",
     "        print(\n",
-    "            f'{smcf.tail(arc):1} -> {smcf.head(arc)}  {flow:3}  / {smcf.capacity(arc):3}       {cost}'\n",
+    "            f\"{smcf.tail(arc):1} -> {smcf.head(arc)}  {flow:3}  / {smcf.capacity(arc):3}       {cost}\"\n",
     "        )\n",
     "\n",
     "\n",
diff --git a/examples/notebook/linear_solver/assignment_groups_mip.ipynb b/examples/notebook/linear_solver/assignment_groups_mip.ipynb
index 396e5d236e..ef3fc24e36 100644
--- a/examples/notebook/linear_solver/assignment_groups_mip.ipynb
+++ b/examples/notebook/linear_solver/assignment_groups_mip.ipynb
@@ -132,7 +132,7 @@
     "\n",
     "    # Solver.\n",
     "    # Create the mip solver with the SCIP backend.\n",
-    "    solver = pywraplp.Solver.CreateSolver('SCIP')\n",
+    "    solver = pywraplp.Solver.CreateSolver(\"SCIP\")\n",
     "    if not solver:\n",
     "        return\n",
     "\n",
@@ -142,27 +142,26 @@
     "    x = {}\n",
     "    for worker in range(num_workers):\n",
     "        for task in range(num_tasks):\n",
-    "            x[worker, task] = solver.BoolVar(f'x[{worker},{task}]')\n",
+    "            x[worker, task] = solver.BoolVar(f\"x[{worker},{task}]\")\n",
     "\n",
     "    # Constraints\n",
     "    # The total size of the tasks each worker takes on is at most total_size_max.\n",
     "    for worker in range(num_workers):\n",
-    "        solver.Add(\n",
-    "            solver.Sum([x[worker, task] for task in range(num_tasks)]) <= 1)\n",
+    "        solver.Add(solver.Sum([x[worker, task] for task in range(num_tasks)]) <= 1)\n",
     "\n",
     "    # Each task is assigned to exactly one worker.\n",
     "    for task in range(num_tasks):\n",
-    "        solver.Add(\n",
-    "            solver.Sum([x[worker, task] for worker in range(num_workers)]) == 1)\n",
+    "        solver.Add(solver.Sum([x[worker, task] for worker in range(num_workers)]) == 1)\n",
     "\n",
     "    # Create variables for each worker, indicating whether they work on some task.\n",
     "    work = {}\n",
     "    for worker in range(num_workers):\n",
-    "        work[worker] = solver.BoolVar(f'work[{worker}]')\n",
+    "        work[worker] = solver.BoolVar(f\"work[{worker}]\")\n",
     "\n",
     "    for worker in range(num_workers):\n",
-    "        solver.Add(work[worker] == solver.Sum(\n",
-    "            [x[worker, task] for task in range(num_tasks)]))\n",
+    "        solver.Add(\n",
+    "            work[worker] == solver.Sum([x[worker, task] for task in range(num_tasks)])\n",
+    "        )\n",
     "\n",
     "    # Group1\n",
     "    constraint_g1 = solver.Constraint(1, 1)\n",
@@ -172,7 +171,7 @@
     "        constraint = solver.Constraint(0, 1)\n",
     "        constraint.SetCoefficient(work[group1[i][0]], 1)\n",
     "        constraint.SetCoefficient(work[group1[i][1]], 1)\n",
-    "        p = solver.BoolVar(f'g1_p{i}')\n",
+    "        p = solver.BoolVar(f\"g1_p{i}\")\n",
     "        constraint.SetCoefficient(p, -2)\n",
     "\n",
     "        constraint_g1.SetCoefficient(p, 1)\n",
@@ -185,7 +184,7 @@
     "        constraint = solver.Constraint(0, 1)\n",
     "        constraint.SetCoefficient(work[group2[i][0]], 1)\n",
     "        constraint.SetCoefficient(work[group2[i][1]], 1)\n",
-    "        p = solver.BoolVar(f'g2_p{i}')\n",
+    "        p = solver.BoolVar(f\"g2_p{i}\")\n",
     "        constraint.SetCoefficient(p, -2)\n",
     "\n",
     "        constraint_g2.SetCoefficient(p, 1)\n",
@@ -198,7 +197,7 @@
     "        constraint = solver.Constraint(0, 1)\n",
     "        constraint.SetCoefficient(work[group3[i][0]], 1)\n",
     "        constraint.SetCoefficient(work[group3[i][1]], 1)\n",
-    "        p = solver.BoolVar(f'g3_p{i}')\n",
+    "        p = solver.BoolVar(f\"g3_p{i}\")\n",
     "        constraint.SetCoefficient(p, -2)\n",
     "\n",
     "        constraint_g3.SetCoefficient(p, 1)\n",
@@ -215,14 +214,16 @@
     "\n",
     "    # Print solution.\n",
     "    if status == pywraplp.Solver.OPTIMAL or status == pywraplp.Solver.FEASIBLE:\n",
-    "        print(f'Total cost = {solver.Objective().Value()}\\n')\n",
+    "        print(f\"Total cost = {solver.Objective().Value()}\\n\")\n",
     "        for worker in range(num_workers):\n",
     "            for task in range(num_tasks):\n",
     "                if x[worker, task].solution_value() > 0.5:\n",
-    "                    print(f'Worker {worker} assigned to task {task}.' +\n",
-    "                          f' Cost: {costs[worker][task]}')\n",
+    "                    print(\n",
+    "                        f\"Worker {worker} assigned to task {task}.\"\n",
+    "                        + f\" Cost: {costs[worker][task]}\"\n",
+    "                    )\n",
     "    else:\n",
-    "        print('No solution found.')\n",
+    "        print(\"No solution found.\")\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/linear_solver/assignment_mb.ipynb b/examples/notebook/linear_solver/assignment_mb.ipynb
index a6b3a94542..c7139b409f 100644
--- a/examples/notebook/linear_solver/assignment_mb.ipynb
+++ b/examples/notebook/linear_solver/assignment_mb.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "MIP example that solves an assignment problem."
    ]
   },
@@ -82,59 +83,76 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
+    "import io\n",
+    "\n",
+    "import pandas as pd\n",
     "\n",
     "from ortools.linear_solver.python import model_builder\n",
     "\n",
     "\n",
     "def main():\n",
     "    # Data\n",
-    "    costs = np.array([\n",
-    "        [90, 80, 75, 70],\n",
-    "        [35, 85, 55, 65],\n",
-    "        [125, 95, 90, 95],\n",
-    "        [45, 110, 95, 115],\n",
-    "        [50, 100, 90, 100],\n",
-    "    ])\n",
-    "    num_workers, num_tasks = costs.shape\n",
+    "    data_str = \"\"\"\n",
+    "  worker  task  cost\n",
+    "      w1    t1    90\n",
+    "      w1    t2    80\n",
+    "      w1    t3    75\n",
+    "      w1    t4    70\n",
+    "      w2    t1    35\n",
+    "      w2    t2    85\n",
+    "      w2    t3    55\n",
+    "      w2    t4    65\n",
+    "      w3    t1   125\n",
+    "      w3    t2    95\n",
+    "      w3    t3    90\n",
+    "      w3    t4    95\n",
+    "      w4    t1    45\n",
+    "      w4    t2   110\n",
+    "      w4    t3    95\n",
+    "      w4    t4   115\n",
+    "      w5    t1    50\n",
+    "      w5    t2   110\n",
+    "      w5    t3    90\n",
+    "      w5    t4   100\n",
+    "  \"\"\"\n",
+    "\n",
+    "    data = pd.read_table(io.StringIO(data_str), sep=r\"\\s+\")\n",
     "\n",
-    "    # Solver\n",
     "    # Create the model.\n",
     "    model = model_builder.ModelBuilder()\n",
     "\n",
     "    # Variables\n",
     "    # x[i, j] is an array of 0-1 variables, which will be 1\n",
     "    # if worker i is assigned to task j.\n",
-    "    x = model.new_bool_var_array(shape=[num_workers, num_tasks], name='x')  # pytype: disable=wrong-arg-types  # numpy-scalars\n",
+    "    x = model.new_bool_var_series(name=\"x\", index=data.index)\n",
     "\n",
     "    # Constraints\n",
     "    # Each worker is assigned to at most 1 task.\n",
-    "    for i in range(num_workers):\n",
-    "        model.add(np.sum(x[i, :]) <= 1)\n",
+    "    for unused_name, tasks in data.groupby(\"worker\"):\n",
+    "        model.add(x[tasks.index].sum() <= 1)\n",
     "\n",
     "    # Each task is assigned to exactly one worker.\n",
-    "    for j in range(num_tasks):\n",
-    "        model.add(np.sum(x[:, j]) == 1)\n",
+    "    for unused_name, workers in data.groupby(\"task\"):\n",
+    "        model.add(x[workers.index].sum() == 1)\n",
     "\n",
     "    # Objective\n",
-    "    model.minimize(np.dot(x.flatten(), costs.flatten()))\n",
+    "    model.minimize(data.cost.dot(x))\n",
     "\n",
     "    # Create the solver with the CP-SAT backend, and solve the model.\n",
-    "    solver = model_builder.ModelSolver('sat')\n",
+    "    solver = model_builder.ModelSolver(\"sat\")\n",
     "    status = solver.solve(model)\n",
     "\n",
     "    # Print solution.\n",
-    "    if (status == model_builder.SolveStatus.OPTIMAL or\n",
-    "            status == model_builder.SolveStatus.FEASIBLE):\n",
-    "        print(f'Total cost = {solver.objective_value}\\n')\n",
-    "        for i in range(num_workers):\n",
-    "            for j in range(num_tasks):\n",
-    "                # Test if x[i,j] is 1 (with tolerance for floating point arithmetic).\n",
-    "                if solver.value(x[i, j]) > 0.5:\n",
-    "                    print(f'Worker {i} assigned to task {j}.' +\n",
-    "                          f' Cost: {costs[i][j]}')\n",
+    "    if (\n",
+    "        status == model_builder.SolveStatus.OPTIMAL\n",
+    "        or status == model_builder.SolveStatus.FEASIBLE\n",
+    "    ):\n",
+    "        print(f\"Total cost = {solver.objective_value}\\n\")\n",
+    "        selected = data.loc[solver.values(x).loc[lambda x: x == 1].index]\n",
+    "        for unused_index, row in selected.iterrows():\n",
+    "            print(f\"{row.task} assigned to {row.worker} with a cost of {row.cost}\")\n",
     "    else:\n",
-    "        print('No solution found.')\n",
+    "        print(\"No solution found.\")\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/linear_solver/assignment_mip.ipynb b/examples/notebook/linear_solver/assignment_mip.ipynb
index 50de3f1c98..7bdd291294 100644
--- a/examples/notebook/linear_solver/assignment_mip.ipynb
+++ b/examples/notebook/linear_solver/assignment_mip.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "MIP example that solves an assignment problem."
    ]
   },
@@ -99,7 +100,7 @@
     "\n",
     "    # Solver\n",
     "    # Create the mip solver with the SCIP backend.\n",
-    "    solver = pywraplp.Solver.CreateSolver('SCIP')\n",
+    "    solver = pywraplp.Solver.CreateSolver(\"SCIP\")\n",
     "\n",
     "    if not solver:\n",
     "        return\n",
@@ -110,7 +111,7 @@
     "    x = {}\n",
     "    for i in range(num_workers):\n",
     "        for j in range(num_tasks):\n",
-    "            x[i, j] = solver.IntVar(0, 1, '')\n",
+    "            x[i, j] = solver.IntVar(0, 1, \"\")\n",
     "\n",
     "    # Constraints\n",
     "    # Each worker is assigned to at most 1 task.\n",
@@ -133,15 +134,14 @@
     "\n",
     "    # Print solution.\n",
     "    if status == pywraplp.Solver.OPTIMAL or status == pywraplp.Solver.FEASIBLE:\n",
-    "        print(f'Total cost = {solver.Objective().Value()}\\n')\n",
+    "        print(f\"Total cost = {solver.Objective().Value()}\\n\")\n",
     "        for i in range(num_workers):\n",
     "            for j in range(num_tasks):\n",
     "                # Test if x[i,j] is 1 (with tolerance for floating point arithmetic).\n",
     "                if x[i, j].solution_value() > 0.5:\n",
-    "                    print(f'Worker {i} assigned to task {j}.' +\n",
-    "                          f' Cost: {costs[i][j]}')\n",
+    "                    print(f\"Worker {i} assigned to task {j}.\" + f\" Cost: {costs[i][j]}\")\n",
     "    else:\n",
-    "        print('No solution found.')\n",
+    "        print(\"No solution found.\")\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/linear_solver/assignment_task_sizes_mip.ipynb b/examples/notebook/linear_solver/assignment_task_sizes_mip.ipynb
index 9e1fb3f00d..8733988bb0 100644
--- a/examples/notebook/linear_solver/assignment_task_sizes_mip.ipynb
+++ b/examples/notebook/linear_solver/assignment_task_sizes_mip.ipynb
@@ -109,7 +109,7 @@
     "\n",
     "    # Solver\n",
     "    # Create the mip solver with the SCIP backend.\n",
-    "    solver = pywraplp.Solver.CreateSolver('SCIP')\n",
+    "    solver = pywraplp.Solver.CreateSolver(\"SCIP\")\n",
     "\n",
     "    if not solver:\n",
     "        return\n",
@@ -120,20 +120,21 @@
     "    x = {}\n",
     "    for worker in range(num_workers):\n",
     "        for task in range(num_tasks):\n",
-    "            x[worker, task] = solver.BoolVar(f'x[{worker},{task}]')\n",
+    "            x[worker, task] = solver.BoolVar(f\"x[{worker},{task}]\")\n",
     "\n",
     "    # Constraints\n",
     "    # The total size of the tasks each worker takes on is at most total_size_max.\n",
     "    for worker in range(num_workers):\n",
     "        solver.Add(\n",
-    "            solver.Sum([\n",
-    "                task_sizes[task] * x[worker, task] for task in range(num_tasks)\n",
-    "            ]) <= total_size_max)\n",
+    "            solver.Sum(\n",
+    "                [task_sizes[task] * x[worker, task] for task in range(num_tasks)]\n",
+    "            )\n",
+    "            <= total_size_max\n",
+    "        )\n",
     "\n",
     "    # Each task is assigned to exactly one worker.\n",
     "    for task in range(num_tasks):\n",
-    "        solver.Add(\n",
-    "            solver.Sum([x[worker, task] for worker in range(num_workers)]) == 1)\n",
+    "        solver.Add(solver.Sum([x[worker, task] for worker in range(num_workers)]) == 1)\n",
     "\n",
     "    # Objective\n",
     "    objective_terms = []\n",
@@ -147,14 +148,16 @@
     "\n",
     "    # Print solution.\n",
     "    if status == pywraplp.Solver.OPTIMAL or status == pywraplp.Solver.FEASIBLE:\n",
-    "        print(f'Total cost = {solver.Objective().Value()}\\n')\n",
+    "        print(f\"Total cost = {solver.Objective().Value()}\\n\")\n",
     "        for worker in range(num_workers):\n",
     "            for task in range(num_tasks):\n",
     "                if x[worker, task].solution_value() > 0.5:\n",
-    "                    print(f'Worker {worker} assigned to task {task}.' +\n",
-    "                          f' Cost: {costs[worker][task]}')\n",
+    "                    print(\n",
+    "                        f\"Worker {worker} assigned to task {task}.\"\n",
+    "                        + f\" Cost: {costs[worker][task]}\"\n",
+    "                    )\n",
     "    else:\n",
-    "        print('No solution found.')\n",
+    "        print(\"No solution found.\")\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/linear_solver/assignment_teams_mip.ipynb b/examples/notebook/linear_solver/assignment_teams_mip.ipynb
index bf18513ecd..2a6922073b 100644
--- a/examples/notebook/linear_solver/assignment_teams_mip.ipynb
+++ b/examples/notebook/linear_solver/assignment_teams_mip.ipynb
@@ -106,7 +106,7 @@
     "\n",
     "    # Solver\n",
     "    # Create the mip solver with the SCIP backend.\n",
-    "    solver = pywraplp.Solver.CreateSolver('SCIP')\n",
+    "    solver = pywraplp.Solver.CreateSolver(\"SCIP\")\n",
     "    if not solver:\n",
     "        return\n",
     "\n",
@@ -116,18 +116,16 @@
     "    x = {}\n",
     "    for worker in range(num_workers):\n",
     "        for task in range(num_tasks):\n",
-    "            x[worker, task] = solver.BoolVar(f'x[{worker},{task}]')\n",
+    "            x[worker, task] = solver.BoolVar(f\"x[{worker},{task}]\")\n",
     "\n",
     "    # Constraints\n",
     "    # Each worker is assigned at most 1 task.\n",
     "    for worker in range(num_workers):\n",
-    "        solver.Add(\n",
-    "            solver.Sum([x[worker, task] for task in range(num_tasks)]) <= 1)\n",
+    "        solver.Add(solver.Sum([x[worker, task] for task in range(num_tasks)]) <= 1)\n",
     "\n",
     "    # Each task is assigned to exactly one worker.\n",
     "    for task in range(num_tasks):\n",
-    "        solver.Add(\n",
-    "            solver.Sum([x[worker, task] for worker in range(num_workers)]) == 1)\n",
+    "        solver.Add(solver.Sum([x[worker, task] for worker in range(num_workers)]) == 1)\n",
     "\n",
     "    # Each team takes at most two tasks.\n",
     "    team1_tasks = []\n",
@@ -154,15 +152,17 @@
     "\n",
     "    # Print solution.\n",
     "    if status == pywraplp.Solver.OPTIMAL or status == pywraplp.Solver.FEASIBLE:\n",
-    "        print(f'Total cost = {solver.Objective().Value()}\\n')\n",
+    "        print(f\"Total cost = {solver.Objective().Value()}\\n\")\n",
     "        for worker in range(num_workers):\n",
     "            for task in range(num_tasks):\n",
     "                if x[worker, task].solution_value() > 0.5:\n",
-    "                    print(f'Worker {worker} assigned to task {task}.' +\n",
-    "                          f' Cost = {costs[worker][task]}')\n",
+    "                    print(\n",
+    "                        f\"Worker {worker} assigned to task {task}.\"\n",
+    "                        + f\" Cost = {costs[worker][task]}\"\n",
+    "                    )\n",
     "    else:\n",
-    "        print('No solution found.')\n",
-    "    print(f'Time = {solver.WallTime()} ms')\n",
+    "        print(\"No solution found.\")\n",
+    "    print(f\"Time = {solver.WallTime()} ms\")\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/linear_solver/basic_example.ipynb b/examples/notebook/linear_solver/basic_example.ipynb
index d81901ea99..c427a5f714 100644
--- a/examples/notebook/linear_solver/basic_example.ipynb
+++ b/examples/notebook/linear_solver/basic_example.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Minimal example to call the GLOP solver."
    ]
   },
@@ -83,27 +84,26 @@
    "outputs": [],
    "source": [
     "from ortools.linear_solver import pywraplp\n",
-    "from ortools.init import pywrapinit\n",
     "\n",
     "\n",
     "def main():\n",
     "    # Create the linear solver with the GLOP backend.\n",
-    "    solver = pywraplp.Solver.CreateSolver('GLOP')\n",
+    "    solver = pywraplp.Solver.CreateSolver(\"GLOP\")\n",
     "    if not solver:\n",
     "        return\n",
     "\n",
     "    # Create the variables x and y.\n",
-    "    x = solver.NumVar(0, 1, 'x')\n",
-    "    y = solver.NumVar(0, 2, 'y')\n",
+    "    x = solver.NumVar(0, 1, \"x\")\n",
+    "    y = solver.NumVar(0, 2, \"y\")\n",
     "\n",
-    "    print('Number of variables =', solver.NumVariables())\n",
+    "    print(\"Number of variables =\", solver.NumVariables())\n",
     "\n",
     "    # Create a linear constraint, 0 <= x + y <= 2.\n",
-    "    ct = solver.Constraint(0, 2, 'ct')\n",
+    "    ct = solver.Constraint(0, 2, \"ct\")\n",
     "    ct.SetCoefficient(x, 1)\n",
     "    ct.SetCoefficient(y, 1)\n",
     "\n",
-    "    print('Number of constraints =', solver.NumConstraints())\n",
+    "    print(\"Number of constraints =\", solver.NumConstraints())\n",
     "\n",
     "    # Create the objective function, 3 * x + y.\n",
     "    objective = solver.Objective()\n",
@@ -113,18 +113,12 @@
     "\n",
     "    solver.Solve()\n",
     "\n",
-    "    print('Solution:')\n",
-    "    print('Objective value =', objective.Value())\n",
-    "    print('x =', x.solution_value())\n",
-    "    print('y =', y.solution_value())\n",
+    "    print(\"Solution:\")\n",
+    "    print(\"Objective value =\", objective.Value())\n",
+    "    print(\"x =\", x.solution_value())\n",
+    "    print(\"y =\", y.solution_value())\n",
     "\n",
     "\n",
-    "pywrapinit.CppBridge.InitLogging('basic_example.py')\n",
-    "cpp_flags = pywrapinit.CppFlags()\n",
-    "cpp_flags.logtostderr = True\n",
-    "cpp_flags.log_prefix = False\n",
-    "pywrapinit.CppBridge.SetFlags(cpp_flags)\n",
-    "\n",
     "main()\n",
     "\n"
    ]
diff --git a/examples/notebook/linear_solver/bin_packing_mb.ipynb b/examples/notebook/linear_solver/bin_packing_mb.ipynb
index b993b3acb2..6a98da73cf 100644
--- a/examples/notebook/linear_solver/bin_packing_mb.ipynb
+++ b/examples/notebook/linear_solver/bin_packing_mb.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Solve a simple bin packing problem using a MIP solver."
    ]
   },
@@ -82,76 +83,103 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
+    "import io\n",
+    "\n",
+    "import pandas as pd\n",
     "\n",
     "from ortools.linear_solver.python import model_builder\n",
     "\n",
     "\n",
     "def create_data_model():\n",
     "    \"\"\"Create the data for the example.\"\"\"\n",
-    "    data = {}\n",
-    "    weights = [48, 30, 19, 36, 36, 27, 42, 42, 36, 24, 30]\n",
-    "    data['weights'] = weights\n",
-    "    data['items'] = list(range(len(weights)))\n",
-    "    data['bins'] = data['items']\n",
-    "    data['bin_capacity'] = 100\n",
-    "    return data\n",
     "\n",
+    "    items_str = \"\"\"\n",
+    "  item  weight\n",
+    "    i1      48\n",
+    "    i2      30\n",
+    "    i3      19\n",
+    "    i4      36\n",
+    "    i5      36\n",
+    "    i6      27\n",
+    "    i7      42\n",
+    "    i8      42\n",
+    "    i9      36\n",
+    "   i10      24\n",
+    "   i11      30\n",
+    "  \"\"\"\n",
+    "\n",
+    "    bins_str = \"\"\"\n",
+    "  bin  capacity\n",
+    "   b1       100\n",
+    "   b2       100\n",
+    "   b3       100\n",
+    "   b4       100\n",
+    "   b5       100\n",
+    "   b6       100\n",
+    "   b7       100\n",
+    "  \"\"\"\n",
+    "\n",
+    "    items = pd.read_table(io.StringIO(items_str), index_col=0, sep=r\"\\s+\")\n",
+    "    bins = pd.read_table(io.StringIO(bins_str), index_col=0, sep=r\"\\s+\")\n",
+    "    return items, bins\n",
     "\n",
     "\n",
     "def main():\n",
-    "    data = create_data_model()\n",
-    "    num_items = len(data['items'])\n",
-    "    num_bins = len(data['bins'])\n",
+    "    items, bins = create_data_model()\n",
     "\n",
     "    # Create the model.\n",
     "    model = model_builder.ModelBuilder()\n",
     "\n",
     "    # Variables\n",
     "    # x[i, j] = 1 if item i is packed in bin j.\n",
-    "    x = model.new_bool_var_array(shape=[num_items, num_bins], name='x')\n",
+    "    items_x_bins = pd.MultiIndex.from_product(\n",
+    "        [items.index, bins.index], names=[\"item\", \"bin\"]\n",
+    "    )\n",
+    "    x = model.new_bool_var_series(name=\"x\", index=items_x_bins)\n",
     "\n",
     "    # y[j] = 1 if bin j is used.\n",
-    "    y = model.new_bool_var_array(shape=[num_bins], name='y')\n",
+    "    y = model.new_bool_var_series(name=\"y\", index=bins.index)\n",
     "\n",
     "    # Constraints\n",
     "    # Each item must be in exactly one bin.\n",
-    "    for i in data['items']:\n",
-    "        model.add(np.sum(x[i, :]) == 1)\n",
+    "    for unused_name, all_copies in x.groupby(\"item\"):\n",
+    "        model.add(x[all_copies.index].sum() == 1)\n",
     "\n",
     "    # The amount packed in each bin cannot exceed its capacity.\n",
-    "    for j in data['bins']:\n",
+    "    for selected_bin in bins.index:\n",
+    "        items_in_bin = x.xs(selected_bin, level=\"bin\")\n",
     "        model.add(\n",
-    "            np.dot(x[:, j], data['weights']) <= data['bin_capacity'] * y[j])\n",
+    "            items_in_bin.dot(items.weight)\n",
+    "            <= bins.loc[selected_bin].capacity * y[selected_bin]\n",
+    "        )\n",
     "\n",
     "    # Objective: minimize the number of bins used.\n",
-    "    model.minimize(np.sum(y))\n",
+    "    model.minimize(y.sum())\n",
     "\n",
     "    # Create the solver with the CP-SAT backend, and solve the model.\n",
-    "    solver = model_builder.ModelSolver('sat')\n",
+    "    solver = model_builder.ModelSolver(\"sat\")\n",
     "    status = solver.solve(model)\n",
     "\n",
     "    if status == model_builder.SolveStatus.OPTIMAL:\n",
-    "        num_bins = 0.\n",
-    "        for j in data['bins']:\n",
-    "            if solver.value(y[j]) == 1:\n",
-    "                bin_items = []\n",
-    "                bin_weight = 0\n",
-    "                for i in data['items']:\n",
-    "                    if solver.value(x[i, j]) > 0:\n",
-    "                        bin_items.append(i)\n",
-    "                        bin_weight += data['weights'][i]\n",
-    "                if bin_weight > 0:\n",
-    "                    num_bins += 1\n",
-    "                    print('Bin number', j)\n",
-    "                    print('  Items packed:', bin_items)\n",
-    "                    print('  Total weight:', bin_weight)\n",
-    "                    print()\n",
+    "        print(f\"Number of bins used = {solver.objective_value}\")\n",
+    "\n",
+    "        x_values = solver.values(x)\n",
+    "        y_values = solver.values(y)\n",
+    "        active_bins = y_values.loc[lambda x: x == 1].index\n",
+    "\n",
+    "        for b in active_bins:\n",
+    "            print(f\"Bin {b}\")\n",
+    "            items_in_bin = x_values.xs(b, level=\"bin\").loc[lambda x: x == 1].index\n",
+    "            for item in items_in_bin:\n",
+    "                print(f\"  Item {item} - weight {items.loc[item].weight}\")\n",
+    "            print(f\"  Packed items weight: {items.loc[items_in_bin].sum().to_string()}\")\n",
+    "            print()\n",
+    "\n",
+    "        print(f\"Total packed weight: {items.weight.sum()}\")\n",
     "        print()\n",
-    "        print('Number of bins used:', num_bins)\n",
-    "        print('Time = ', solver.wall_time, ' seconds')\n",
+    "        print(f\"Time = {solver.wall_time} seconds\")\n",
     "    else:\n",
-    "        print('The problem does not have an optimal solution.')\n",
+    "        print(\"The problem does not have an optimal solution.\")\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/linear_solver/bin_packing_mip.ipynb b/examples/notebook/linear_solver/bin_packing_mip.ipynb
index cae82bf39f..8b9b282784 100644
--- a/examples/notebook/linear_solver/bin_packing_mip.ipynb
+++ b/examples/notebook/linear_solver/bin_packing_mip.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Solve a simple bin packing problem using a MIP solver."
    ]
   },
@@ -89,10 +90,10 @@
     "    \"\"\"Create the data for the example.\"\"\"\n",
     "    data = {}\n",
     "    weights = [48, 30, 19, 36, 36, 27, 42, 42, 36, 24, 30]\n",
-    "    data['weights'] = weights\n",
-    "    data['items'] = list(range(len(weights)))\n",
-    "    data['bins'] = data['items']\n",
-    "    data['bin_capacity'] = 100\n",
+    "    data[\"weights\"] = weights\n",
+    "    data[\"items\"] = list(range(len(weights)))\n",
+    "    data[\"bins\"] = data[\"items\"]\n",
+    "    data[\"bin_capacity\"] = 100\n",
     "    return data\n",
     "\n",
     "\n",
@@ -101,7 +102,7 @@
     "    data = create_data_model()\n",
     "\n",
     "    # Create the mip solver with the SCIP backend.\n",
-    "    solver = pywraplp.Solver.CreateSolver('SCIP')\n",
+    "    solver = pywraplp.Solver.CreateSolver(\"SCIP\")\n",
     "\n",
     "    if not solver:\n",
     "        return\n",
@@ -109,52 +110,53 @@
     "    # Variables\n",
     "    # x[i, j] = 1 if item i is packed in bin j.\n",
     "    x = {}\n",
-    "    for i in data['items']:\n",
-    "        for j in data['bins']:\n",
-    "            x[(i, j)] = solver.IntVar(0, 1, 'x_%i_%i' % (i, j))\n",
+    "    for i in data[\"items\"]:\n",
+    "        for j in data[\"bins\"]:\n",
+    "            x[(i, j)] = solver.IntVar(0, 1, \"x_%i_%i\" % (i, j))\n",
     "\n",
     "    # y[j] = 1 if bin j is used.\n",
     "    y = {}\n",
-    "    for j in data['bins']:\n",
-    "        y[j] = solver.IntVar(0, 1, 'y[%i]' % j)\n",
+    "    for j in data[\"bins\"]:\n",
+    "        y[j] = solver.IntVar(0, 1, \"y[%i]\" % j)\n",
     "\n",
     "    # Constraints\n",
     "    # Each item must be in exactly one bin.\n",
-    "    for i in data['items']:\n",
-    "        solver.Add(sum(x[i, j] for j in data['bins']) == 1)\n",
+    "    for i in data[\"items\"]:\n",
+    "        solver.Add(sum(x[i, j] for j in data[\"bins\"]) == 1)\n",
     "\n",
     "    # The amount packed in each bin cannot exceed its capacity.\n",
-    "    for j in data['bins']:\n",
+    "    for j in data[\"bins\"]:\n",
     "        solver.Add(\n",
-    "            sum(x[(i, j)] * data['weights'][i] for i in data['items']) <= y[j] *\n",
-    "            data['bin_capacity'])\n",
+    "            sum(x[(i, j)] * data[\"weights\"][i] for i in data[\"items\"])\n",
+    "            <= y[j] * data[\"bin_capacity\"]\n",
+    "        )\n",
     "\n",
     "    # Objective: minimize the number of bins used.\n",
-    "    solver.Minimize(solver.Sum([y[j] for j in data['bins']]))\n",
+    "    solver.Minimize(solver.Sum([y[j] for j in data[\"bins\"]]))\n",
     "\n",
     "    status = solver.Solve()\n",
     "\n",
     "    if status == pywraplp.Solver.OPTIMAL:\n",
     "        num_bins = 0\n",
-    "        for j in data['bins']:\n",
+    "        for j in data[\"bins\"]:\n",
     "            if y[j].solution_value() == 1:\n",
     "                bin_items = []\n",
     "                bin_weight = 0\n",
-    "                for i in data['items']:\n",
+    "                for i in data[\"items\"]:\n",
     "                    if x[i, j].solution_value() > 0:\n",
     "                        bin_items.append(i)\n",
-    "                        bin_weight += data['weights'][i]\n",
+    "                        bin_weight += data[\"weights\"][i]\n",
     "                if bin_items:\n",
     "                    num_bins += 1\n",
-    "                    print('Bin number', j)\n",
-    "                    print('  Items packed:', bin_items)\n",
-    "                    print('  Total weight:', bin_weight)\n",
+    "                    print(\"Bin number\", j)\n",
+    "                    print(\"  Items packed:\", bin_items)\n",
+    "                    print(\"  Total weight:\", bin_weight)\n",
     "                    print()\n",
     "        print()\n",
-    "        print('Number of bins used:', num_bins)\n",
-    "        print('Time = ', solver.WallTime(), ' milliseconds')\n",
+    "        print(\"Number of bins used:\", num_bins)\n",
+    "        print(\"Time = \", solver.WallTime(), \" milliseconds\")\n",
     "    else:\n",
-    "        print('The problem does not have an optimal solution.')\n",
+    "        print(\"The problem does not have an optimal solution.\")\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/linear_solver/integer_programming_example.ipynb b/examples/notebook/linear_solver/integer_programming_example.ipynb
index fc75c3f2b5..692ea2e089 100644
--- a/examples/notebook/linear_solver/integer_programming_example.ipynb
+++ b/examples/notebook/linear_solver/integer_programming_example.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Small example to illustrate solving a MIP problem."
    ]
   },
@@ -88,14 +89,14 @@
     "def IntegerProgrammingExample():\n",
     "    \"\"\"Integer programming sample.\"\"\"\n",
     "    # Create the mip solver with the SCIP backend.\n",
-    "    solver = pywraplp.Solver.CreateSolver('SCIP')\n",
+    "    solver = pywraplp.Solver.CreateSolver(\"SCIP\")\n",
     "    if not solver:\n",
     "        return\n",
     "\n",
     "    # x, y, and z are non-negative integer variables.\n",
-    "    x = solver.IntVar(0.0, solver.infinity(), 'x')\n",
-    "    y = solver.IntVar(0.0, solver.infinity(), 'y')\n",
-    "    z = solver.IntVar(0.0, solver.infinity(), 'z')\n",
+    "    x = solver.IntVar(0.0, solver.infinity(), \"x\")\n",
+    "    y = solver.IntVar(0.0, solver.infinity(), \"y\")\n",
+    "    z = solver.IntVar(0.0, solver.infinity(), \"z\")\n",
     "\n",
     "    # 2*x + 7*y + 3*z <= 50\n",
     "    constraint0 = solver.Constraint(-solver.infinity(), 50)\n",
@@ -125,11 +126,11 @@
     "    # Solve the problem and print the solution.\n",
     "    solver.Solve()\n",
     "    # Print the objective value of the solution.\n",
-    "    print('Maximum objective function value = %d' % solver.Objective().Value())\n",
+    "    print(\"Maximum objective function value = %d\" % solver.Objective().Value())\n",
     "    print()\n",
     "    # Print the value of each variable in the solution.\n",
     "    for variable in [x, y, z]:\n",
-    "        print('%s = %d' % (variable.name(), variable.solution_value()))\n",
+    "        print(\"%s = %d\" % (variable.name(), variable.solution_value()))\n",
     "\n",
     "\n",
     "IntegerProgrammingExample()\n",
diff --git a/examples/notebook/linear_solver/linear_programming_example.ipynb b/examples/notebook/linear_solver/linear_programming_example.ipynb
index 907d51a6f0..f2604f5f90 100644
--- a/examples/notebook/linear_solver/linear_programming_example.ipynb
+++ b/examples/notebook/linear_solver/linear_programming_example.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Linear optimization example."
    ]
   },
@@ -88,15 +89,15 @@
     "def LinearProgrammingExample():\n",
     "    \"\"\"Linear programming sample.\"\"\"\n",
     "    # Instantiate a Glop solver, naming it LinearExample.\n",
-    "    solver = pywraplp.Solver.CreateSolver('GLOP')\n",
+    "    solver = pywraplp.Solver.CreateSolver(\"GLOP\")\n",
     "    if not solver:\n",
     "        return\n",
     "\n",
     "    # Create the two variables and let them take on any non-negative value.\n",
-    "    x = solver.NumVar(0, solver.infinity(), 'x')\n",
-    "    y = solver.NumVar(0, solver.infinity(), 'y')\n",
+    "    x = solver.NumVar(0, solver.infinity(), \"x\")\n",
+    "    y = solver.NumVar(0, solver.infinity(), \"y\")\n",
     "\n",
-    "    print('Number of variables =', solver.NumVariables())\n",
+    "    print(\"Number of variables =\", solver.NumVariables())\n",
     "\n",
     "    # Constraint 0: x + 2y <= 14.\n",
     "    solver.Add(x + 2 * y <= 14.0)\n",
@@ -107,7 +108,7 @@
     "    # Constraint 2: x - y <= 2.\n",
     "    solver.Add(x - y <= 2.0)\n",
     "\n",
-    "    print('Number of constraints =', solver.NumConstraints())\n",
+    "    print(\"Number of constraints =\", solver.NumConstraints())\n",
     "\n",
     "    # Objective function: 3x + 4y.\n",
     "    solver.Maximize(3 * x + 4 * y)\n",
@@ -116,16 +117,16 @@
     "    status = solver.Solve()\n",
     "\n",
     "    if status == pywraplp.Solver.OPTIMAL:\n",
-    "        print('Solution:')\n",
-    "        print('Objective value =', solver.Objective().Value())\n",
-    "        print('x =', x.solution_value())\n",
-    "        print('y =', y.solution_value())\n",
+    "        print(\"Solution:\")\n",
+    "        print(\"Objective value =\", solver.Objective().Value())\n",
+    "        print(\"x =\", x.solution_value())\n",
+    "        print(\"y =\", y.solution_value())\n",
     "    else:\n",
-    "        print('The problem does not have an optimal solution.')\n",
+    "        print(\"The problem does not have an optimal solution.\")\n",
     "\n",
-    "    print('\\nAdvanced usage:')\n",
-    "    print('Problem solved in %f milliseconds' % solver.wall_time())\n",
-    "    print('Problem solved in %d iterations' % solver.iterations())\n",
+    "    print(\"\\nAdvanced usage:\")\n",
+    "    print(\"Problem solved in %f milliseconds\" % solver.wall_time())\n",
+    "    print(\"Problem solved in %d iterations\" % solver.iterations())\n",
     "\n",
     "\n",
     "LinearProgrammingExample()\n",
diff --git a/examples/notebook/linear_solver/mip_var_array.ipynb b/examples/notebook/linear_solver/mip_var_array.ipynb
index 68291b30b6..5b0b1dd6b8 100644
--- a/examples/notebook/linear_solver/mip_var_array.ipynb
+++ b/examples/notebook/linear_solver/mip_var_array.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "MIP example that uses a variable array."
    ]
   },
@@ -88,16 +89,16 @@
     "def create_data_model():\n",
     "    \"\"\"Stores the data for the problem.\"\"\"\n",
     "    data = {}\n",
-    "    data['constraint_coeffs'] = [\n",
+    "    data[\"constraint_coeffs\"] = [\n",
     "        [5, 7, 9, 2, 1],\n",
     "        [18, 4, -9, 10, 12],\n",
     "        [4, 7, 3, 8, 5],\n",
     "        [5, 13, 16, 3, -7],\n",
     "    ]\n",
-    "    data['bounds'] = [250, 285, 211, 315]\n",
-    "    data['obj_coeffs'] = [7, 8, 2, 9, 6]\n",
-    "    data['num_vars'] = 5\n",
-    "    data['num_constraints'] = 4\n",
+    "    data[\"bounds\"] = [250, 285, 211, 315]\n",
+    "    data[\"obj_coeffs\"] = [7, 8, 2, 9, 6]\n",
+    "    data[\"num_vars\"] = 5\n",
+    "    data[\"num_constraints\"] = 4\n",
     "    return data\n",
     "\n",
     "\n",
@@ -105,21 +106,21 @@
     "def main():\n",
     "    data = create_data_model()\n",
     "    # Create the mip solver with the SCIP backend.\n",
-    "    solver = pywraplp.Solver.CreateSolver('SCIP')\n",
+    "    solver = pywraplp.Solver.CreateSolver(\"SCIP\")\n",
     "    if not solver:\n",
     "        return\n",
     "\n",
     "    infinity = solver.infinity()\n",
     "    x = {}\n",
-    "    for j in range(data['num_vars']):\n",
-    "        x[j] = solver.IntVar(0, infinity, 'x[%i]' % j)\n",
-    "    print('Number of variables =', solver.NumVariables())\n",
+    "    for j in range(data[\"num_vars\"]):\n",
+    "        x[j] = solver.IntVar(0, infinity, \"x[%i]\" % j)\n",
+    "    print(\"Number of variables =\", solver.NumVariables())\n",
     "\n",
-    "    for i in range(data['num_constraints']):\n",
-    "        constraint = solver.RowConstraint(0, data['bounds'][i], '')\n",
-    "        for j in range(data['num_vars']):\n",
-    "            constraint.SetCoefficient(x[j], data['constraint_coeffs'][i][j])\n",
-    "    print('Number of constraints =', solver.NumConstraints())\n",
+    "    for i in range(data[\"num_constraints\"]):\n",
+    "        constraint = solver.RowConstraint(0, data[\"bounds\"][i], \"\")\n",
+    "        for j in range(data[\"num_vars\"]):\n",
+    "            constraint.SetCoefficient(x[j], data[\"constraint_coeffs\"][i][j])\n",
+    "    print(\"Number of constraints =\", solver.NumConstraints())\n",
     "    # In Python, you can also set the constraints as follows.\n",
     "    # for i in range(data['num_constraints']):\n",
     "    #  constraint_expr = \\\n",
@@ -127,8 +128,8 @@
     "    #  solver.Add(sum(constraint_expr) <= data['bounds'][i])\n",
     "\n",
     "    objective = solver.Objective()\n",
-    "    for j in range(data['num_vars']):\n",
-    "        objective.SetCoefficient(x[j], data['obj_coeffs'][j])\n",
+    "    for j in range(data[\"num_vars\"]):\n",
+    "        objective.SetCoefficient(x[j], data[\"obj_coeffs\"][j])\n",
     "    objective.SetMaximization()\n",
     "    # In Python, you can also set the objective as follows.\n",
     "    # obj_expr = [data['obj_coeffs'][j] * x[j] for j in range(data['num_vars'])]\n",
@@ -137,15 +138,15 @@
     "    status = solver.Solve()\n",
     "\n",
     "    if status == pywraplp.Solver.OPTIMAL:\n",
-    "        print('Objective value =', solver.Objective().Value())\n",
-    "        for j in range(data['num_vars']):\n",
-    "            print(x[j].name(), ' = ', x[j].solution_value())\n",
+    "        print(\"Objective value =\", solver.Objective().Value())\n",
+    "        for j in range(data[\"num_vars\"]):\n",
+    "            print(x[j].name(), \" = \", x[j].solution_value())\n",
     "        print()\n",
-    "        print('Problem solved in %f milliseconds' % solver.wall_time())\n",
-    "        print('Problem solved in %d iterations' % solver.iterations())\n",
-    "        print('Problem solved in %d branch-and-bound nodes' % solver.nodes())\n",
+    "        print(\"Problem solved in %f milliseconds\" % solver.wall_time())\n",
+    "        print(\"Problem solved in %d iterations\" % solver.iterations())\n",
+    "        print(\"Problem solved in %d branch-and-bound nodes\" % solver.nodes())\n",
     "    else:\n",
-    "        print('The problem does not have an optimal solution.')\n",
+    "        print(\"The problem does not have an optimal solution.\")\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/linear_solver/multiple_knapsack_mip.ipynb b/examples/notebook/linear_solver/multiple_knapsack_mip.ipynb
index f56b790cbb..fc595783db 100644
--- a/examples/notebook/linear_solver/multiple_knapsack_mip.ipynb
+++ b/examples/notebook/linear_solver/multiple_knapsack_mip.ipynb
@@ -88,74 +88,71 @@
     "\n",
     "def main():\n",
     "    data = {}\n",
-    "    data['weights'] = [\n",
-    "        48, 30, 42, 36, 36, 48, 42, 42, 36, 24, 30, 30, 42, 36, 36\n",
-    "    ]\n",
-    "    data['values'] = [\n",
-    "        10, 30, 25, 50, 35, 30, 15, 40, 30, 35, 45, 10, 20, 30, 25\n",
-    "    ]\n",
-    "    assert len(data['weights']) == len(data['values'])\n",
-    "    data['num_items'] = len(data['weights'])\n",
-    "    data['all_items'] = range(data['num_items'])\n",
+    "    data[\"weights\"] = [48, 30, 42, 36, 36, 48, 42, 42, 36, 24, 30, 30, 42, 36, 36]\n",
+    "    data[\"values\"] = [10, 30, 25, 50, 35, 30, 15, 40, 30, 35, 45, 10, 20, 30, 25]\n",
+    "    assert len(data[\"weights\"]) == len(data[\"values\"])\n",
+    "    data[\"num_items\"] = len(data[\"weights\"])\n",
+    "    data[\"all_items\"] = range(data[\"num_items\"])\n",
     "\n",
-    "    data['bin_capacities'] = [100, 100, 100, 100, 100]\n",
-    "    data['num_bins'] = len(data['bin_capacities'])\n",
-    "    data['all_bins'] = range(data['num_bins'])\n",
+    "    data[\"bin_capacities\"] = [100, 100, 100, 100, 100]\n",
+    "    data[\"num_bins\"] = len(data[\"bin_capacities\"])\n",
+    "    data[\"all_bins\"] = range(data[\"num_bins\"])\n",
     "\n",
     "    # Create the mip solver with the SCIP backend.\n",
-    "    solver = pywraplp.Solver.CreateSolver('SCIP')\n",
+    "    solver = pywraplp.Solver.CreateSolver(\"SCIP\")\n",
     "    if solver is None:\n",
-    "        print('SCIP solver unavailable.')\n",
+    "        print(\"SCIP solver unavailable.\")\n",
     "        return\n",
     "\n",
     "    # Variables.\n",
     "    # x[i, b] = 1 if item i is packed in bin b.\n",
     "    x = {}\n",
-    "    for i in data['all_items']:\n",
-    "        for b in data['all_bins']:\n",
-    "            x[i, b] = solver.BoolVar(f'x_{i}_{b}')\n",
+    "    for i in data[\"all_items\"]:\n",
+    "        for b in data[\"all_bins\"]:\n",
+    "            x[i, b] = solver.BoolVar(f\"x_{i}_{b}\")\n",
     "\n",
     "    # Constraints.\n",
     "    # Each item is assigned to at most one bin.\n",
-    "    for i in data['all_items']:\n",
-    "        solver.Add(sum(x[i, b] for b in data['all_bins']) <= 1)\n",
+    "    for i in data[\"all_items\"]:\n",
+    "        solver.Add(sum(x[i, b] for b in data[\"all_bins\"]) <= 1)\n",
     "\n",
     "    # The amount packed in each bin cannot exceed its capacity.\n",
-    "    for b in data['all_bins']:\n",
+    "    for b in data[\"all_bins\"]:\n",
     "        solver.Add(\n",
-    "            sum(x[i, b] * data['weights'][i]\n",
-    "                for i in data['all_items']) <= data['bin_capacities'][b])\n",
+    "            sum(x[i, b] * data[\"weights\"][i] for i in data[\"all_items\"])\n",
+    "            <= data[\"bin_capacities\"][b]\n",
+    "        )\n",
     "\n",
     "    # Objective.\n",
     "    # Maximize total value of packed items.\n",
     "    objective = solver.Objective()\n",
-    "    for i in data['all_items']:\n",
-    "        for b in data['all_bins']:\n",
-    "            objective.SetCoefficient(x[i, b], data['values'][i])\n",
+    "    for i in data[\"all_items\"]:\n",
+    "        for b in data[\"all_bins\"]:\n",
+    "            objective.SetCoefficient(x[i, b], data[\"values\"][i])\n",
     "    objective.SetMaximization()\n",
     "\n",
     "    status = solver.Solve()\n",
     "\n",
     "    if status == pywraplp.Solver.OPTIMAL:\n",
-    "        print(f'Total packed value: {objective.Value()}')\n",
+    "        print(f\"Total packed value: {objective.Value()}\")\n",
     "        total_weight = 0\n",
-    "        for b in data['all_bins']:\n",
-    "            print(f'Bin {b}')\n",
+    "        for b in data[\"all_bins\"]:\n",
+    "            print(f\"Bin {b}\")\n",
     "            bin_weight = 0\n",
     "            bin_value = 0\n",
-    "            for i in data['all_items']:\n",
+    "            for i in data[\"all_items\"]:\n",
     "                if x[i, b].solution_value() > 0:\n",
     "                    print(\n",
     "                        f\"Item {i} weight: {data['weights'][i]} value: {data['values'][i]}\"\n",
     "                    )\n",
-    "                    bin_weight += data['weights'][i]\n",
-    "                    bin_value += data['values'][i]\n",
-    "            print(f'Packed bin weight: {bin_weight}')\n",
-    "            print(f'Packed bin value: {bin_value}\\n')\n",
+    "                    bin_weight += data[\"weights\"][i]\n",
+    "                    bin_value += data[\"values\"][i]\n",
+    "            print(f\"Packed bin weight: {bin_weight}\")\n",
+    "            print(f\"Packed bin value: {bin_value}\\n\")\n",
     "            total_weight += bin_weight\n",
-    "        print(f'Total packed weight: {total_weight}')\n",
+    "        print(f\"Total packed weight: {total_weight}\")\n",
     "    else:\n",
-    "        print('The problem does not have an optimal solution.')\n",
+    "        print(\"The problem does not have an optimal solution.\")\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/linear_solver/simple_lp_program.ipynb b/examples/notebook/linear_solver/simple_lp_program.ipynb
index 8143f0b9d9..d73b98e8bf 100644
--- a/examples/notebook/linear_solver/simple_lp_program.ipynb
+++ b/examples/notebook/linear_solver/simple_lp_program.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Minimal example to call the GLOP solver."
    ]
   },
@@ -87,16 +88,16 @@
     "\n",
     "def main():\n",
     "    # Create the linear solver with the GLOP backend.\n",
-    "    solver = pywraplp.Solver.CreateSolver('GLOP')\n",
+    "    solver = pywraplp.Solver.CreateSolver(\"GLOP\")\n",
     "    if not solver:\n",
     "        return\n",
     "\n",
     "    infinity = solver.infinity()\n",
     "    # Create the variables x and y.\n",
-    "    x = solver.NumVar(0.0, infinity, 'x')\n",
-    "    y = solver.NumVar(0.0, infinity, 'y')\n",
+    "    x = solver.NumVar(0.0, infinity, \"x\")\n",
+    "    y = solver.NumVar(0.0, infinity, \"y\")\n",
     "\n",
-    "    print('Number of variables =', solver.NumVariables())\n",
+    "    print(\"Number of variables =\", solver.NumVariables())\n",
     "\n",
     "    # x + 7 * y <= 17.5.\n",
     "    solver.Add(x + 7 * y <= 17.5)\n",
@@ -104,25 +105,25 @@
     "    # x <= 3.5.\n",
     "    solver.Add(x <= 3.5)\n",
     "\n",
-    "    print('Number of constraints =', solver.NumConstraints())\n",
+    "    print(\"Number of constraints =\", solver.NumConstraints())\n",
     "\n",
     "    # Maximize x + 10 * y.\n",
     "    solver.Maximize(x + 10 * y)\n",
     "\n",
-    "    print(f'Solving with {solver.SolverVersion()}')\n",
+    "    print(f\"Solving with {solver.SolverVersion()}\")\n",
     "    status = solver.Solve()\n",
     "\n",
     "    if status == pywraplp.Solver.OPTIMAL:\n",
-    "        print('Solution:')\n",
-    "        print('Objective value =', solver.Objective().Value())\n",
-    "        print('x =', x.solution_value())\n",
-    "        print('y =', y.solution_value())\n",
+    "        print(\"Solution:\")\n",
+    "        print(\"Objective value =\", solver.Objective().Value())\n",
+    "        print(\"x =\", x.solution_value())\n",
+    "        print(\"y =\", y.solution_value())\n",
     "    else:\n",
-    "        print('The problem does not have an optimal solution.')\n",
+    "        print(\"The problem does not have an optimal solution.\")\n",
     "\n",
-    "    print('\\nAdvanced usage:')\n",
-    "    print('Problem solved in %f milliseconds' % solver.wall_time())\n",
-    "    print('Problem solved in %d iterations' % solver.iterations())\n",
+    "    print(\"\\nAdvanced usage:\")\n",
+    "    print(\"Problem solved in %f milliseconds\" % solver.wall_time())\n",
+    "    print(\"Problem solved in %d iterations\" % solver.iterations())\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/linear_solver/simple_lp_program_mb.ipynb b/examples/notebook/linear_solver/simple_lp_program_mb.ipynb
index 4f742877f6..63739fe614 100644
--- a/examples/notebook/linear_solver/simple_lp_program_mb.ipynb
+++ b/examples/notebook/linear_solver/simple_lp_program_mb.ipynb
@@ -93,10 +93,10 @@
     "    model = model_builder.ModelBuilder()\n",
     "\n",
     "    # Create the variables x and y.\n",
-    "    x = model.new_num_var(0.0, math.inf, 'x')\n",
-    "    y = model.new_num_var(0.0, math.inf, 'y')\n",
+    "    x = model.new_num_var(0.0, math.inf, \"x\")\n",
+    "    y = model.new_num_var(0.0, math.inf, \"y\")\n",
     "\n",
-    "    print('Number of variables =', model.num_variables)\n",
+    "    print(\"Number of variables =\", model.num_variables)\n",
     "\n",
     "    # x + 7 * y <= 17.5.\n",
     "    ct = model.add(x + 7 * y <= 17.5)\n",
@@ -104,28 +104,28 @@
     "    # x <= 3.5.\n",
     "    model.add(x <= 3.5)\n",
     "\n",
-    "    print('Number of constraints =', model.num_constraints)\n",
+    "    print(\"Number of constraints =\", model.num_constraints)\n",
     "\n",
     "    # Maximize x + 10 * y.\n",
     "    model.maximize(x + 10 * y)\n",
     "\n",
     "    # Create the solver with the GLOP backend, and solve the model.\n",
-    "    solver = model_builder.ModelSolver('glop')\n",
+    "    solver = model_builder.ModelSolver(\"glop\")\n",
     "    status = solver.solve(model)\n",
     "\n",
     "    if status == model_builder.SolveStatus.OPTIMAL:\n",
-    "        print('Solution:')\n",
-    "        print('Objective value =', solver.objective_value)\n",
-    "        print('x =', solver.value(x))\n",
-    "        print('y =', solver.value(y))\n",
+    "        print(\"Solution:\")\n",
+    "        print(\"Objective value =\", solver.objective_value)\n",
+    "        print(\"x =\", solver.value(x))\n",
+    "        print(\"y =\", solver.value(y))\n",
     "\n",
-    "        print('dual_value(ct) =', solver.dual_value(ct))\n",
-    "        print('reduced_cost(x) =', solver.reduced_cost(x))\n",
+    "        print(\"dual_value(ct) =\", solver.dual_value(ct))\n",
+    "        print(\"reduced_cost(x) =\", solver.reduced_cost(x))\n",
     "    else:\n",
-    "        print('The problem does not have an optimal solution.')\n",
+    "        print(\"The problem does not have an optimal solution.\")\n",
     "\n",
-    "    print('\\nAdvanced usage:')\n",
-    "    print('Problem solved in %f seconds' % solver.wall_time)\n",
+    "    print(\"\\nAdvanced usage:\")\n",
+    "    print(\"Problem solved in %f seconds\" % solver.wall_time)\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/linear_solver/simple_mip_program.ipynb b/examples/notebook/linear_solver/simple_mip_program.ipynb
index fe16491469..54bbe63a16 100644
--- a/examples/notebook/linear_solver/simple_mip_program.ipynb
+++ b/examples/notebook/linear_solver/simple_mip_program.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Integer programming examples that show how to use the APIs."
    ]
   },
@@ -87,16 +88,16 @@
     "\n",
     "def main():\n",
     "    # Create the mip solver with the SCIP backend.\n",
-    "    solver = pywraplp.Solver.CreateSolver('SAT')\n",
+    "    solver = pywraplp.Solver.CreateSolver(\"SAT\")\n",
     "    if not solver:\n",
     "        return\n",
     "\n",
     "    infinity = solver.infinity()\n",
     "    # x and y are integer non-negative variables.\n",
-    "    x = solver.IntVar(0.0, infinity, 'x')\n",
-    "    y = solver.IntVar(0.0, infinity, 'y')\n",
+    "    x = solver.IntVar(0.0, infinity, \"x\")\n",
+    "    y = solver.IntVar(0.0, infinity, \"y\")\n",
     "\n",
-    "    print('Number of variables =', solver.NumVariables())\n",
+    "    print(\"Number of variables =\", solver.NumVariables())\n",
     "\n",
     "    # x + 7 * y <= 17.5.\n",
     "    solver.Add(x + 7 * y <= 17.5)\n",
@@ -104,26 +105,26 @@
     "    # x <= 3.5.\n",
     "    solver.Add(x <= 3.5)\n",
     "\n",
-    "    print('Number of constraints =', solver.NumConstraints())\n",
+    "    print(\"Number of constraints =\", solver.NumConstraints())\n",
     "\n",
     "    # Maximize x + 10 * y.\n",
     "    solver.Maximize(x + 10 * y)\n",
     "\n",
-    "    print(f'Solving with {solver.SolverVersion()}')\n",
+    "    print(f\"Solving with {solver.SolverVersion()}\")\n",
     "    status = solver.Solve()\n",
     "\n",
     "    if status == pywraplp.Solver.OPTIMAL:\n",
-    "        print('Solution:')\n",
-    "        print('Objective value =', solver.Objective().Value())\n",
-    "        print('x =', x.solution_value())\n",
-    "        print('y =', y.solution_value())\n",
+    "        print(\"Solution:\")\n",
+    "        print(\"Objective value =\", solver.Objective().Value())\n",
+    "        print(\"x =\", x.solution_value())\n",
+    "        print(\"y =\", y.solution_value())\n",
     "    else:\n",
-    "        print('The problem does not have an optimal solution.')\n",
+    "        print(\"The problem does not have an optimal solution.\")\n",
     "\n",
-    "    print('\\nAdvanced usage:')\n",
-    "    print('Problem solved in %f milliseconds' % solver.wall_time())\n",
-    "    print('Problem solved in %d iterations' % solver.iterations())\n",
-    "    print('Problem solved in %d branch-and-bound nodes' % solver.nodes())\n",
+    "    print(\"\\nAdvanced usage:\")\n",
+    "    print(\"Problem solved in %f milliseconds\" % solver.wall_time())\n",
+    "    print(\"Problem solved in %d iterations\" % solver.iterations())\n",
+    "    print(\"Problem solved in %d branch-and-bound nodes\" % solver.nodes())\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/linear_solver/simple_mip_program_mb.ipynb b/examples/notebook/linear_solver/simple_mip_program_mb.ipynb
index abe134e277..547f157144 100644
--- a/examples/notebook/linear_solver/simple_mip_program_mb.ipynb
+++ b/examples/notebook/linear_solver/simple_mip_program_mb.ipynb
@@ -93,10 +93,10 @@
     "    model = model_builder.ModelBuilder()\n",
     "\n",
     "    # x and y are integer non-negative variables.\n",
-    "    x = model.new_int_var(0.0, math.inf, 'x')\n",
-    "    y = model.new_int_var(0.0, math.inf, 'y')\n",
+    "    x = model.new_int_var(0.0, math.inf, \"x\")\n",
+    "    y = model.new_int_var(0.0, math.inf, \"y\")\n",
     "\n",
-    "    print('Number of variables =', model.num_variables)\n",
+    "    print(\"Number of variables =\", model.num_variables)\n",
     "\n",
     "    # x + 7 * y <= 17.5.\n",
     "    model.add(x + 7 * y <= 17.5)\n",
@@ -104,25 +104,25 @@
     "    # x <= 3.5.\n",
     "    model.add(x <= 3.5)\n",
     "\n",
-    "    print('Number of constraints =', model.num_constraints)\n",
+    "    print(\"Number of constraints =\", model.num_constraints)\n",
     "\n",
     "    # Maximize x + 10 * y.\n",
     "    model.maximize(x + 10 * y)\n",
     "\n",
     "    # Create the solver with the SCIP backend, and solve the model.\n",
-    "    solver = model_builder.ModelSolver('scip')\n",
+    "    solver = model_builder.ModelSolver(\"scip\")\n",
     "    status = solver.solve(model)\n",
     "\n",
     "    if status == model_builder.SolveStatus.OPTIMAL:\n",
-    "        print('Solution:')\n",
-    "        print('Objective value =', solver.objective_value)\n",
-    "        print('x =', solver.value(x))\n",
-    "        print('y =', solver.value(y))\n",
+    "        print(\"Solution:\")\n",
+    "        print(\"Objective value =\", solver.objective_value)\n",
+    "        print(\"x =\", solver.value(x))\n",
+    "        print(\"y =\", solver.value(y))\n",
     "    else:\n",
-    "        print('The problem does not have an optimal solution.')\n",
+    "        print(\"The problem does not have an optimal solution.\")\n",
     "\n",
-    "    print('\\nAdvanced usage:')\n",
-    "    print('Problem solved in %f seconds' % solver.wall_time)\n",
+    "    print(\"\\nAdvanced usage:\")\n",
+    "    print(\"Problem solved in %f seconds\" % solver.wall_time)\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/linear_solver/stigler_diet.ipynb b/examples/notebook/linear_solver/stigler_diet.ipynb
index b3b4413ebe..1df445d673 100644
--- a/examples/notebook/linear_solver/stigler_diet.ipynb
+++ b/examples/notebook/linear_solver/stigler_diet.ipynb
@@ -94,208 +94,111 @@
     "    # Instantiate the data problem.\n",
     "    # Nutrient minimums.\n",
     "    nutrients = [\n",
-    "        ['Calories (kcal)', 3],\n",
-    "        ['Protein (g)', 70],\n",
-    "        ['Calcium (g)', 0.8],\n",
-    "        ['Iron (mg)', 12],\n",
-    "        ['Vitamin A (KIU)', 5],\n",
-    "        ['Vitamin B1 (mg)', 1.8],\n",
-    "        ['Vitamin B2 (mg)', 2.7],\n",
-    "        ['Niacin (mg)', 18],\n",
-    "        ['Vitamin C (mg)', 75],\n",
+    "        [\"Calories (kcal)\", 3],\n",
+    "        [\"Protein (g)\", 70],\n",
+    "        [\"Calcium (g)\", 0.8],\n",
+    "        [\"Iron (mg)\", 12],\n",
+    "        [\"Vitamin A (KIU)\", 5],\n",
+    "        [\"Vitamin B1 (mg)\", 1.8],\n",
+    "        [\"Vitamin B2 (mg)\", 2.7],\n",
+    "        [\"Niacin (mg)\", 18],\n",
+    "        [\"Vitamin C (mg)\", 75],\n",
     "    ]\n",
     "\n",
     "    # Commodity, Unit, 1939 price (cents), Calories (kcal), Protein (g),\n",
     "    # Calcium (g), Iron (mg), Vitamin A (KIU), Vitamin B1 (mg), Vitamin B2 (mg),\n",
     "    # Niacin (mg), Vitamin C (mg)\n",
     "    data = [\n",
-    "        [\n",
-    "            'Wheat Flour (Enriched)', '10 lb.', 36, 44.7, 1411, 2, 365, 0, 55.4,\n",
-    "            33.3, 441, 0\n",
-    "        ],\n",
-    "        ['Macaroni', '1 lb.', 14.1, 11.6, 418, 0.7, 54, 0, 3.2, 1.9, 68, 0],\n",
-    "        [\n",
-    "            'Wheat Cereal (Enriched)', '28 oz.', 24.2, 11.8, 377, 14.4, 175, 0,\n",
-    "            14.4, 8.8, 114, 0\n",
-    "        ],\n",
-    "        ['Corn Flakes', '8 oz.', 7.1, 11.4, 252, 0.1, 56, 0, 13.5, 2.3, 68, 0],\n",
-    "        [\n",
-    "            'Corn Meal', '1 lb.', 4.6, 36.0, 897, 1.7, 99, 30.9, 17.4, 7.9, 106,\n",
-    "            0\n",
-    "        ],\n",
-    "        [\n",
-    "            'Hominy Grits', '24 oz.', 8.5, 28.6, 680, 0.8, 80, 0, 10.6, 1.6,\n",
-    "            110, 0\n",
-    "        ],\n",
-    "        ['Rice', '1 lb.', 7.5, 21.2, 460, 0.6, 41, 0, 2, 4.8, 60, 0],\n",
-    "        ['Rolled Oats', '1 lb.', 7.1, 25.3, 907, 5.1, 341, 0, 37.1, 8.9, 64, 0],\n",
-    "        [\n",
-    "            'White Bread (Enriched)', '1 lb.', 7.9, 15.0, 488, 2.5, 115, 0,\n",
-    "            13.8, 8.5, 126, 0\n",
-    "        ],\n",
-    "        [\n",
-    "            'Whole Wheat Bread', '1 lb.', 9.1, 12.2, 484, 2.7, 125, 0, 13.9,\n",
-    "            6.4, 160, 0\n",
-    "        ],\n",
-    "        ['Rye Bread', '1 lb.', 9.1, 12.4, 439, 1.1, 82, 0, 9.9, 3, 66, 0],\n",
-    "        ['Pound Cake', '1 lb.', 24.8, 8.0, 130, 0.4, 31, 18.9, 2.8, 3, 17, 0],\n",
-    "        ['Soda Crackers', '1 lb.', 15.1, 12.5, 288, 0.5, 50, 0, 0, 0, 0, 0],\n",
-    "        ['Milk', '1 qt.', 11, 6.1, 310, 10.5, 18, 16.8, 4, 16, 7, 177],\n",
-    "        [\n",
-    "            'Evaporated Milk (can)', '14.5 oz.', 6.7, 8.4, 422, 15.1, 9, 26, 3,\n",
-    "            23.5, 11, 60\n",
-    "        ],\n",
-    "        ['Butter', '1 lb.', 30.8, 10.8, 9, 0.2, 3, 44.2, 0, 0.2, 2, 0],\n",
-    "        ['Oleomargarine', '1 lb.', 16.1, 20.6, 17, 0.6, 6, 55.8, 0.2, 0, 0, 0],\n",
-    "        ['Eggs', '1 doz.', 32.6, 2.9, 238, 1.0, 52, 18.6, 2.8, 6.5, 1, 0],\n",
-    "        [\n",
-    "            'Cheese (Cheddar)', '1 lb.', 24.2, 7.4, 448, 16.4, 19, 28.1, 0.8,\n",
-    "            10.3, 4, 0\n",
-    "        ],\n",
-    "        ['Cream', '1/2 pt.', 14.1, 3.5, 49, 1.7, 3, 16.9, 0.6, 2.5, 0, 17],\n",
-    "        [\n",
-    "            'Peanut Butter', '1 lb.', 17.9, 15.7, 661, 1.0, 48, 0, 9.6, 8.1,\n",
-    "            471, 0\n",
-    "        ],\n",
-    "        ['Mayonnaise', '1/2 pt.', 16.7, 8.6, 18, 0.2, 8, 2.7, 0.4, 0.5, 0, 0],\n",
-    "        ['Crisco', '1 lb.', 20.3, 20.1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
-    "        ['Lard', '1 lb.', 9.8, 41.7, 0, 0, 0, 0.2, 0, 0.5, 5, 0],\n",
-    "        [\n",
-    "            'Sirloin Steak', '1 lb.', 39.6, 2.9, 166, 0.1, 34, 0.2, 2.1, 2.9,\n",
-    "            69, 0\n",
-    "        ],\n",
-    "        ['Round Steak', '1 lb.', 36.4, 2.2, 214, 0.1, 32, 0.4, 2.5, 2.4, 87, 0],\n",
-    "        ['Rib Roast', '1 lb.', 29.2, 3.4, 213, 0.1, 33, 0, 0, 2, 0, 0],\n",
-    "        ['Chuck Roast', '1 lb.', 22.6, 3.6, 309, 0.2, 46, 0.4, 1, 4, 120, 0],\n",
-    "        ['Plate', '1 lb.', 14.6, 8.5, 404, 0.2, 62, 0, 0.9, 0, 0, 0],\n",
-    "        [\n",
-    "            'Liver (Beef)', '1 lb.', 26.8, 2.2, 333, 0.2, 139, 169.2, 6.4, 50.8,\n",
-    "            316, 525\n",
-    "        ],\n",
-    "        ['Leg of Lamb', '1 lb.', 27.6, 3.1, 245, 0.1, 20, 0, 2.8, 3.9, 86, 0],\n",
-    "        [\n",
-    "            'Lamb Chops (Rib)', '1 lb.', 36.6, 3.3, 140, 0.1, 15, 0, 1.7, 2.7,\n",
-    "            54, 0\n",
-    "        ],\n",
-    "        ['Pork Chops', '1 lb.', 30.7, 3.5, 196, 0.2, 30, 0, 17.4, 2.7, 60, 0],\n",
-    "        [\n",
-    "            'Pork Loin Roast', '1 lb.', 24.2, 4.4, 249, 0.3, 37, 0, 18.2, 3.6,\n",
-    "            79, 0\n",
-    "        ],\n",
-    "        ['Bacon', '1 lb.', 25.6, 10.4, 152, 0.2, 23, 0, 1.8, 1.8, 71, 0],\n",
-    "        ['Ham, smoked', '1 lb.', 27.4, 6.7, 212, 0.2, 31, 0, 9.9, 3.3, 50, 0],\n",
-    "        ['Salt Pork', '1 lb.', 16, 18.8, 164, 0.1, 26, 0, 1.4, 1.8, 0, 0],\n",
-    "        [\n",
-    "            'Roasting Chicken', '1 lb.', 30.3, 1.8, 184, 0.1, 30, 0.1, 0.9, 1.8,\n",
-    "            68, 46\n",
-    "        ],\n",
-    "        ['Veal Cutlets', '1 lb.', 42.3, 1.7, 156, 0.1, 24, 0, 1.4, 2.4, 57, 0],\n",
-    "        [\n",
-    "            'Salmon, Pink (can)', '16 oz.', 13, 5.8, 705, 6.8, 45, 3.5, 1, 4.9,\n",
-    "            209, 0\n",
-    "        ],\n",
-    "        ['Apples', '1 lb.', 4.4, 5.8, 27, 0.5, 36, 7.3, 3.6, 2.7, 5, 544],\n",
-    "        ['Bananas', '1 lb.', 6.1, 4.9, 60, 0.4, 30, 17.4, 2.5, 3.5, 28, 498],\n",
-    "        ['Lemons', '1 doz.', 26, 1.0, 21, 0.5, 14, 0, 0.5, 0, 4, 952],\n",
-    "        ['Oranges', '1 doz.', 30.9, 2.2, 40, 1.1, 18, 11.1, 3.6, 1.3, 10, 1998],\n",
-    "        ['Green Beans', '1 lb.', 7.1, 2.4, 138, 3.7, 80, 69, 4.3, 5.8, 37, 862],\n",
-    "        ['Cabbage', '1 lb.', 3.7, 2.6, 125, 4.0, 36, 7.2, 9, 4.5, 26, 5369],\n",
-    "        ['Carrots', '1 bunch', 4.7, 2.7, 73, 2.8, 43, 188.5, 6.1, 4.3, 89, 608],\n",
-    "        ['Celery', '1 stalk', 7.3, 0.9, 51, 3.0, 23, 0.9, 1.4, 1.4, 9, 313],\n",
-    "        ['Lettuce', '1 head', 8.2, 0.4, 27, 1.1, 22, 112.4, 1.8, 3.4, 11, 449],\n",
-    "        ['Onions', '1 lb.', 3.6, 5.8, 166, 3.8, 59, 16.6, 4.7, 5.9, 21, 1184],\n",
-    "        [\n",
-    "            'Potatoes', '15 lb.', 34, 14.3, 336, 1.8, 118, 6.7, 29.4, 7.1, 198,\n",
-    "            2522\n",
-    "        ],\n",
-    "        ['Spinach', '1 lb.', 8.1, 1.1, 106, 0, 138, 918.4, 5.7, 13.8, 33, 2755],\n",
-    "        [\n",
-    "            'Sweet Potatoes', '1 lb.', 5.1, 9.6, 138, 2.7, 54, 290.7, 8.4, 5.4,\n",
-    "            83, 1912\n",
-    "        ],\n",
-    "        [\n",
-    "            'Peaches (can)', 'No. 2 1/2', 16.8, 3.7, 20, 0.4, 10, 21.5, 0.5, 1,\n",
-    "            31, 196\n",
-    "        ],\n",
-    "        [\n",
-    "            'Pears (can)', 'No. 2 1/2', 20.4, 3.0, 8, 0.3, 8, 0.8, 0.8, 0.8, 5,\n",
-    "            81\n",
-    "        ],\n",
-    "        [\n",
-    "            'Pineapple (can)', 'No. 2 1/2', 21.3, 2.4, 16, 0.4, 8, 2, 2.8, 0.8,\n",
-    "            7, 399\n",
-    "        ],\n",
-    "        [\n",
-    "            'Asparagus (can)', 'No. 2', 27.7, 0.4, 33, 0.3, 12, 16.3, 1.4, 2.1,\n",
-    "            17, 272\n",
-    "        ],\n",
-    "        [\n",
-    "            'Green Beans (can)', 'No. 2', 10, 1.0, 54, 2, 65, 53.9, 1.6, 4.3,\n",
-    "            32, 431\n",
-    "        ],\n",
-    "        [\n",
-    "            'Pork and Beans (can)', '16 oz.', 7.1, 7.5, 364, 4, 134, 3.5, 8.3,\n",
-    "            7.7, 56, 0\n",
-    "        ],\n",
-    "        ['Corn (can)', 'No. 2', 10.4, 5.2, 136, 0.2, 16, 12, 1.6, 2.7, 42, 218],\n",
-    "        [\n",
-    "            'Peas (can)', 'No. 2', 13.8, 2.3, 136, 0.6, 45, 34.9, 4.9, 2.5, 37,\n",
-    "            370\n",
-    "        ],\n",
-    "        [\n",
-    "            'Tomatoes (can)', 'No. 2', 8.6, 1.3, 63, 0.7, 38, 53.2, 3.4, 2.5,\n",
-    "            36, 1253\n",
-    "        ],\n",
-    "        [\n",
-    "            'Tomato Soup (can)', '10 1/2 oz.', 7.6, 1.6, 71, 0.6, 43, 57.9, 3.5,\n",
-    "            2.4, 67, 862\n",
-    "        ],\n",
-    "        [\n",
-    "            'Peaches, Dried', '1 lb.', 15.7, 8.5, 87, 1.7, 173, 86.8, 1.2, 4.3,\n",
-    "            55, 57\n",
-    "        ],\n",
-    "        [\n",
-    "            'Prunes, Dried', '1 lb.', 9, 12.8, 99, 2.5, 154, 85.7, 3.9, 4.3, 65,\n",
-    "            257\n",
-    "        ],\n",
-    "        [\n",
-    "            'Raisins, Dried', '15 oz.', 9.4, 13.5, 104, 2.5, 136, 4.5, 6.3, 1.4,\n",
-    "            24, 136\n",
-    "        ],\n",
-    "        [\n",
-    "            'Peas, Dried', '1 lb.', 7.9, 20.0, 1367, 4.2, 345, 2.9, 28.7, 18.4,\n",
-    "            162, 0\n",
-    "        ],\n",
-    "        [\n",
-    "            'Lima Beans, Dried', '1 lb.', 8.9, 17.4, 1055, 3.7, 459, 5.1, 26.9,\n",
-    "            38.2, 93, 0\n",
-    "        ],\n",
-    "        [\n",
-    "            'Navy Beans, Dried', '1 lb.', 5.9, 26.9, 1691, 11.4, 792, 0, 38.4,\n",
-    "            24.6, 217, 0\n",
-    "        ],\n",
-    "        ['Coffee', '1 lb.', 22.4, 0, 0, 0, 0, 0, 4, 5.1, 50, 0],\n",
-    "        ['Tea', '1/4 lb.', 17.4, 0, 0, 0, 0, 0, 0, 2.3, 42, 0],\n",
-    "        ['Cocoa', '8 oz.', 8.6, 8.7, 237, 3, 72, 0, 2, 11.9, 40, 0],\n",
-    "        ['Chocolate', '8 oz.', 16.2, 8.0, 77, 1.3, 39, 0, 0.9, 3.4, 14, 0],\n",
-    "        ['Sugar', '10 lb.', 51.7, 34.9, 0, 0, 0, 0, 0, 0, 0, 0],\n",
-    "        ['Corn Syrup', '24 oz.', 13.7, 14.7, 0, 0.5, 74, 0, 0, 0, 5, 0],\n",
-    "        ['Molasses', '18 oz.', 13.6, 9.0, 0, 10.3, 244, 0, 1.9, 7.5, 146, 0],\n",
-    "        [\n",
-    "            'Strawberry Preserves', '1 lb.', 20.5, 6.4, 11, 0.4, 7, 0.2, 0.2,\n",
-    "            0.4, 3, 0\n",
-    "        ],\n",
+    "        # fmt: off\n",
+    "      ['Wheat Flour (Enriched)', '10 lb.', 36, 44.7, 1411, 2, 365, 0, 55.4, 33.3, 441, 0],\n",
+    "      ['Macaroni', '1 lb.', 14.1, 11.6, 418, 0.7, 54, 0, 3.2, 1.9, 68, 0],\n",
+    "      ['Wheat Cereal (Enriched)', '28 oz.', 24.2, 11.8, 377, 14.4, 175, 0, 14.4, 8.8, 114, 0],\n",
+    "      ['Corn Flakes', '8 oz.', 7.1, 11.4, 252, 0.1, 56, 0, 13.5, 2.3, 68, 0],\n",
+    "      ['Corn Meal', '1 lb.', 4.6, 36.0, 897, 1.7, 99, 30.9, 17.4, 7.9, 106, 0],\n",
+    "      ['Hominy Grits', '24 oz.', 8.5, 28.6, 680, 0.8, 80, 0, 10.6, 1.6, 110, 0],\n",
+    "      ['Rice', '1 lb.', 7.5, 21.2, 460, 0.6, 41, 0, 2, 4.8, 60, 0],\n",
+    "      ['Rolled Oats', '1 lb.', 7.1, 25.3, 907, 5.1, 341, 0, 37.1, 8.9, 64, 0],\n",
+    "      ['White Bread (Enriched)', '1 lb.', 7.9, 15.0, 488, 2.5, 115, 0, 13.8, 8.5, 126, 0],\n",
+    "      ['Whole Wheat Bread', '1 lb.', 9.1, 12.2, 484, 2.7, 125, 0, 13.9, 6.4, 160, 0],\n",
+    "      ['Rye Bread', '1 lb.', 9.1, 12.4, 439, 1.1, 82, 0, 9.9, 3, 66, 0],\n",
+    "      ['Pound Cake', '1 lb.', 24.8, 8.0, 130, 0.4, 31, 18.9, 2.8, 3, 17, 0],\n",
+    "      ['Soda Crackers', '1 lb.', 15.1, 12.5, 288, 0.5, 50, 0, 0, 0, 0, 0],\n",
+    "      ['Milk', '1 qt.', 11, 6.1, 310, 10.5, 18, 16.8, 4, 16, 7, 177],\n",
+    "      ['Evaporated Milk (can)', '14.5 oz.', 6.7, 8.4, 422, 15.1, 9, 26, 3, 23.5, 11, 60],\n",
+    "      ['Butter', '1 lb.', 30.8, 10.8, 9, 0.2, 3, 44.2, 0, 0.2, 2, 0],\n",
+    "      ['Oleomargarine', '1 lb.', 16.1, 20.6, 17, 0.6, 6, 55.8, 0.2, 0, 0, 0],\n",
+    "      ['Eggs', '1 doz.', 32.6, 2.9, 238, 1.0, 52, 18.6, 2.8, 6.5, 1, 0],\n",
+    "      ['Cheese (Cheddar)', '1 lb.', 24.2, 7.4, 448, 16.4, 19, 28.1, 0.8, 10.3, 4, 0],\n",
+    "      ['Cream', '1/2 pt.', 14.1, 3.5, 49, 1.7, 3, 16.9, 0.6, 2.5, 0, 17],\n",
+    "      ['Peanut Butter', '1 lb.', 17.9, 15.7, 661, 1.0, 48, 0, 9.6, 8.1, 471, 0],\n",
+    "      ['Mayonnaise', '1/2 pt.', 16.7, 8.6, 18, 0.2, 8, 2.7, 0.4, 0.5, 0, 0],\n",
+    "      ['Crisco', '1 lb.', 20.3, 20.1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+    "      ['Lard', '1 lb.', 9.8, 41.7, 0, 0, 0, 0.2, 0, 0.5, 5, 0],\n",
+    "      ['Sirloin Steak', '1 lb.', 39.6, 2.9, 166, 0.1, 34, 0.2, 2.1, 2.9, 69, 0],\n",
+    "      ['Round Steak', '1 lb.', 36.4, 2.2, 214, 0.1, 32, 0.4, 2.5, 2.4, 87, 0],\n",
+    "      ['Rib Roast', '1 lb.', 29.2, 3.4, 213, 0.1, 33, 0, 0, 2, 0, 0],\n",
+    "      ['Chuck Roast', '1 lb.', 22.6, 3.6, 309, 0.2, 46, 0.4, 1, 4, 120, 0],\n",
+    "      ['Plate', '1 lb.', 14.6, 8.5, 404, 0.2, 62, 0, 0.9, 0, 0, 0],\n",
+    "      ['Liver (Beef)', '1 lb.', 26.8, 2.2, 333, 0.2, 139, 169.2, 6.4, 50.8, 316, 525],\n",
+    "      ['Leg of Lamb', '1 lb.', 27.6, 3.1, 245, 0.1, 20, 0, 2.8, 3.9, 86, 0],\n",
+    "      ['Lamb Chops (Rib)', '1 lb.', 36.6, 3.3, 140, 0.1, 15, 0, 1.7, 2.7, 54, 0],\n",
+    "      ['Pork Chops', '1 lb.', 30.7, 3.5, 196, 0.2, 30, 0, 17.4, 2.7, 60, 0],\n",
+    "      ['Pork Loin Roast', '1 lb.', 24.2, 4.4, 249, 0.3, 37, 0, 18.2, 3.6, 79, 0],\n",
+    "      ['Bacon', '1 lb.', 25.6, 10.4, 152, 0.2, 23, 0, 1.8, 1.8, 71, 0],\n",
+    "      ['Ham, smoked', '1 lb.', 27.4, 6.7, 212, 0.2, 31, 0, 9.9, 3.3, 50, 0],\n",
+    "      ['Salt Pork', '1 lb.', 16, 18.8, 164, 0.1, 26, 0, 1.4, 1.8, 0, 0],\n",
+    "      ['Roasting Chicken', '1 lb.', 30.3, 1.8, 184, 0.1, 30, 0.1, 0.9, 1.8, 68, 46],\n",
+    "      ['Veal Cutlets', '1 lb.', 42.3, 1.7, 156, 0.1, 24, 0, 1.4, 2.4, 57, 0],\n",
+    "      ['Salmon, Pink (can)', '16 oz.', 13, 5.8, 705, 6.8, 45, 3.5, 1, 4.9, 209, 0],\n",
+    "      ['Apples', '1 lb.', 4.4, 5.8, 27, 0.5, 36, 7.3, 3.6, 2.7, 5, 544],\n",
+    "      ['Bananas', '1 lb.', 6.1, 4.9, 60, 0.4, 30, 17.4, 2.5, 3.5, 28, 498],\n",
+    "      ['Lemons', '1 doz.', 26, 1.0, 21, 0.5, 14, 0, 0.5, 0, 4, 952],\n",
+    "      ['Oranges', '1 doz.', 30.9, 2.2, 40, 1.1, 18, 11.1, 3.6, 1.3, 10, 1998],\n",
+    "      ['Green Beans', '1 lb.', 7.1, 2.4, 138, 3.7, 80, 69, 4.3, 5.8, 37, 862],\n",
+    "      ['Cabbage', '1 lb.', 3.7, 2.6, 125, 4.0, 36, 7.2, 9, 4.5, 26, 5369],\n",
+    "      ['Carrots', '1 bunch', 4.7, 2.7, 73, 2.8, 43, 188.5, 6.1, 4.3, 89, 608],\n",
+    "      ['Celery', '1 stalk', 7.3, 0.9, 51, 3.0, 23, 0.9, 1.4, 1.4, 9, 313],\n",
+    "      ['Lettuce', '1 head', 8.2, 0.4, 27, 1.1, 22, 112.4, 1.8, 3.4, 11, 449],\n",
+    "      ['Onions', '1 lb.', 3.6, 5.8, 166, 3.8, 59, 16.6, 4.7, 5.9, 21, 1184],\n",
+    "      ['Potatoes', '15 lb.', 34, 14.3, 336, 1.8, 118, 6.7, 29.4, 7.1, 198, 2522],\n",
+    "      ['Spinach', '1 lb.', 8.1, 1.1, 106, 0, 138, 918.4, 5.7, 13.8, 33, 2755],\n",
+    "      ['Sweet Potatoes', '1 lb.', 5.1, 9.6, 138, 2.7, 54, 290.7, 8.4, 5.4, 83, 1912],\n",
+    "      ['Peaches (can)', 'No. 2 1/2', 16.8, 3.7, 20, 0.4, 10, 21.5, 0.5, 1, 31, 196],\n",
+    "      ['Pears (can)', 'No. 2 1/2', 20.4, 3.0, 8, 0.3, 8, 0.8, 0.8, 0.8, 5, 81],\n",
+    "      ['Pineapple (can)', 'No. 2 1/2', 21.3, 2.4, 16, 0.4, 8, 2, 2.8, 0.8, 7, 399],\n",
+    "      ['Asparagus (can)', 'No. 2', 27.7, 0.4, 33, 0.3, 12, 16.3, 1.4, 2.1, 17, 272],\n",
+    "      ['Green Beans (can)', 'No. 2', 10, 1.0, 54, 2, 65, 53.9, 1.6, 4.3, 32, 431],\n",
+    "      ['Pork and Beans (can)', '16 oz.', 7.1, 7.5, 364, 4, 134, 3.5, 8.3, 7.7, 56, 0],\n",
+    "      ['Corn (can)', 'No. 2', 10.4, 5.2, 136, 0.2, 16, 12, 1.6, 2.7, 42, 218],\n",
+    "      ['Peas (can)', 'No. 2', 13.8, 2.3, 136, 0.6, 45, 34.9, 4.9, 2.5, 37, 370],\n",
+    "      ['Tomatoes (can)', 'No. 2', 8.6, 1.3, 63, 0.7, 38, 53.2, 3.4, 2.5, 36, 1253],\n",
+    "      ['Tomato Soup (can)', '10 1/2 oz.', 7.6, 1.6, 71, 0.6, 43, 57.9, 3.5, 2.4, 67, 862],\n",
+    "      ['Peaches, Dried', '1 lb.', 15.7, 8.5, 87, 1.7, 173, 86.8, 1.2, 4.3, 55, 57],\n",
+    "      ['Prunes, Dried', '1 lb.', 9, 12.8, 99, 2.5, 154, 85.7, 3.9, 4.3, 65, 257],\n",
+    "      ['Raisins, Dried', '15 oz.', 9.4, 13.5, 104, 2.5, 136, 4.5, 6.3, 1.4, 24, 136],\n",
+    "      ['Peas, Dried', '1 lb.', 7.9, 20.0, 1367, 4.2, 345, 2.9, 28.7, 18.4, 162, 0],\n",
+    "      ['Lima Beans, Dried', '1 lb.', 8.9, 17.4, 1055, 3.7, 459, 5.1, 26.9, 38.2, 93, 0],\n",
+    "      ['Navy Beans, Dried', '1 lb.', 5.9, 26.9, 1691, 11.4, 792, 0, 38.4, 24.6, 217, 0],\n",
+    "      ['Coffee', '1 lb.', 22.4, 0, 0, 0, 0, 0, 4, 5.1, 50, 0],\n",
+    "      ['Tea', '1/4 lb.', 17.4, 0, 0, 0, 0, 0, 0, 2.3, 42, 0],\n",
+    "      ['Cocoa', '8 oz.', 8.6, 8.7, 237, 3, 72, 0, 2, 11.9, 40, 0],\n",
+    "      ['Chocolate', '8 oz.', 16.2, 8.0, 77, 1.3, 39, 0, 0.9, 3.4, 14, 0],\n",
+    "      ['Sugar', '10 lb.', 51.7, 34.9, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+    "      ['Corn Syrup', '24 oz.', 13.7, 14.7, 0, 0.5, 74, 0, 0, 0, 5, 0],\n",
+    "      ['Molasses', '18 oz.', 13.6, 9.0, 0, 10.3, 244, 0, 1.9, 7.5, 146, 0],\n",
+    "      ['Strawberry Preserves', '1 lb.', 20.5, 6.4, 11, 0.4, 7, 0.2, 0.2, 0.4, 3, 0],\n",
+    "        # fmt: on\n",
     "    ]\n",
     "\n",
     "    # Instantiate a Glop solver and naming it.\n",
-    "    solver = pywraplp.Solver.CreateSolver('GLOP')\n",
+    "    solver = pywraplp.Solver.CreateSolver(\"GLOP\")\n",
     "    if not solver:\n",
     "        return\n",
     "\n",
     "    # Declare an array to hold our variables.\n",
     "    foods = [solver.NumVar(0.0, solver.infinity(), item[0]) for item in data]\n",
     "\n",
-    "    print('Number of variables =', solver.NumVariables())\n",
+    "    print(\"Number of variables =\", solver.NumVariables())\n",
     "\n",
     "    # Create the constraints, one per nutrient.\n",
     "    constraints = []\n",
@@ -304,7 +207,7 @@
     "        for j, item in enumerate(data):\n",
     "            constraints[i].SetCoefficient(foods[j], item[i + 3])\n",
     "\n",
-    "    print('Number of constraints =', solver.NumConstraints())\n",
+    "    print(\"Number of constraints =\", solver.NumConstraints())\n",
     "\n",
     "    # Objective function: Minimize the sum of (price-normalized) foods.\n",
     "    objective = solver.Objective()\n",
@@ -316,31 +219,32 @@
     "\n",
     "    # Check that the problem has an optimal solution.\n",
     "    if status != solver.OPTIMAL:\n",
-    "        print('The problem does not have an optimal solution!')\n",
+    "        print(\"The problem does not have an optimal solution!\")\n",
     "        if status == solver.FEASIBLE:\n",
-    "            print('A potentially suboptimal solution was found.')\n",
+    "            print(\"A potentially suboptimal solution was found.\")\n",
     "        else:\n",
-    "            print('The solver could not solve the problem.')\n",
+    "            print(\"The solver could not solve the problem.\")\n",
     "            exit(1)\n",
     "\n",
     "    # Display the amounts (in dollars) to purchase of each food.\n",
     "    nutrients_result = [0] * len(nutrients)\n",
-    "    print('\\nAnnual Foods:')\n",
+    "    print(\"\\nAnnual Foods:\")\n",
     "    for i, food in enumerate(foods):\n",
     "        if food.solution_value() > 0.0:\n",
-    "            print('{}: ${}'.format(data[i][0], 365. * food.solution_value()))\n",
+    "            print(\"{}: ${}\".format(data[i][0], 365.0 * food.solution_value()))\n",
     "            for j, _ in enumerate(nutrients):\n",
     "                nutrients_result[j] += data[i][j + 3] * food.solution_value()\n",
-    "    print('\\nOptimal annual price: ${:.4f}'.format(365. * objective.Value()))\n",
+    "    print(\"\\nOptimal annual price: ${:.4f}\".format(365.0 * objective.Value()))\n",
     "\n",
-    "    print('\\nNutrients per day:')\n",
+    "    print(\"\\nNutrients per day:\")\n",
     "    for i, nutrient in enumerate(nutrients):\n",
-    "        print('{}: {:.2f} (min {})'.format(nutrient[0], nutrients_result[i],\n",
-    "                                           nutrient[1]))\n",
+    "        print(\n",
+    "            \"{}: {:.2f} (min {})\".format(nutrient[0], nutrients_result[i], nutrient[1])\n",
+    "        )\n",
     "\n",
-    "    print('\\nAdvanced usage:')\n",
-    "    print('Problem solved in ', solver.wall_time(), ' milliseconds')\n",
-    "    print('Problem solved in ', solver.iterations(), ' iterations')\n",
+    "    print(\"\\nAdvanced usage:\")\n",
+    "    print(\"Problem solved in \", solver.wall_time(), \" milliseconds\")\n",
+    "    print(\"Problem solved in \", solver.iterations(), \" iterations\")\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/pdlp/simple_pdlp_program.ipynb b/examples/notebook/pdlp/simple_pdlp_program.ipynb
index 5bac6ab268..4a4a3b6375 100644
--- a/examples/notebook/pdlp/simple_pdlp_program.ipynb
+++ b/examples/notebook/pdlp/simple_pdlp_program.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Solves a simple LP using PDLP's direct Python API.\n",
     "\n",
     "Note: The direct API is generally for advanced use cases. It is matrix-based,\n",
@@ -93,24 +94,24 @@
     "\n",
     "from ortools.pdlp import solve_log_pb2\n",
     "from ortools.pdlp import solvers_pb2\n",
-    "from ortools.pdlp.python import pywrap_pdlp\n",
-    "from ortools.init import pywrapinit\n",
+    "from ortools.pdlp.python import pdlp\n",
+    "from ortools.init.python import init\n",
     "\n",
     "\n",
-    "def simple_lp() -> pywrap_pdlp.QuadraticProgram:\n",
+    "def simple_lp() -> pdlp.QuadraticProgram:\n",
     "    \"\"\"Returns a small LP.\n",
     "\n",
-    "  min 5.5 x_0 - 2 x_1 - x_2 +   x_3 - 14 s.t.\n",
-    "      2 x_0 +     x_1 +   x_2 + 2 x_3  = 12\n",
-    "        x_0 +             x_2          <=  7\n",
-    "      4 x_0                            >=  -4\n",
-    "     -1 <=            1.5 x_2 -   x_3  <= 1\n",
-    "    -infinity <= x_0 <= infinity\n",
-    "           -2 <= x_1 <= infinity\n",
-    "    -infinity <= x_2 <= 6\n",
-    "          2.5 <= x_3 <= 3.5\n",
-    "  \"\"\"\n",
-    "    lp = pywrap_pdlp.QuadraticProgram()\n",
+    "    min 5.5 x_0 - 2 x_1 - x_2 +   x_3 - 14 s.t.\n",
+    "        2 x_0 +     x_1 +   x_2 + 2 x_3  = 12\n",
+    "          x_0 +             x_2          <=  7\n",
+    "        4 x_0                            >=  -4\n",
+    "       -1 <=            1.5 x_2 -   x_3  <= 1\n",
+    "      -infinity <= x_0 <= infinity\n",
+    "             -2 <= x_1 <= infinity\n",
+    "      -infinity <= x_2 <= 6\n",
+    "            2.5 <= x_3 <= 3.5\n",
+    "    \"\"\"\n",
+    "    lp = pdlp.QuadraticProgram()\n",
     "    lp.objective_offset = -14\n",
     "    lp.objective_vector = [5.5, -2, -1, 1]\n",
     "    lp.constraint_lower_bounds = [12, -np.inf, -4, -1]\n",
@@ -120,8 +121,9 @@
     "    # Most use cases should initialize the sparse constraint matrix without\n",
     "    # constructing a dense matrix first! We use a np.array here for convenience\n",
     "    # only.\n",
-    "    constraint_matrix = np.array([[2, 1, 1, 2], [1, 0, 1, 0], [4, 0, 0, 0],\n",
-    "                                  [0, 0, 1.5, -1]])\n",
+    "    constraint_matrix = np.array(\n",
+    "        [[2, 1, 1, 2], [1, 0, 1, 0], [4, 0, 0, 0], [0, 0, 1.5, -1]]\n",
+    "    )\n",
     "    lp.constraint_matrix = scipy.sparse.csc_matrix(constraint_matrix)\n",
     "    return lp\n",
     "\n",
@@ -140,41 +142,41 @@
     "\n",
     "    # Call the main solve function. Note that a quirk of the pywrap11 API forces\n",
     "    # us to serialize the `params` and deserialize the `solve_log` proto messages.\n",
-    "    result = pywrap_pdlp.primal_dual_hybrid_gradient(simple_lp(),\n",
-    "                                                     params.SerializeToString())\n",
+    "    result = pdlp.primal_dual_hybrid_gradient(simple_lp(), params.SerializeToString())\n",
     "    solve_log = solve_log_pb2.SolveLog.FromString(result.solve_log_str)\n",
     "\n",
     "    if solve_log.termination_reason == solve_log_pb2.TERMINATION_REASON_OPTIMAL:\n",
-    "        print('Solve successful')\n",
+    "        print(\"Solve successful\")\n",
     "    else:\n",
     "        print(\n",
-    "            'Solve not successful. Status:',\n",
-    "            solve_log_pb2.TerminationReason.Name(solve_log.termination_reason))\n",
+    "            \"Solve not successful. Status:\",\n",
+    "            solve_log_pb2.TerminationReason.Name(solve_log.termination_reason),\n",
+    "        )\n",
     "\n",
     "    # Solutions vectors are always returned. *However*, their interpretation\n",
     "    # depends on termination_reason! See primal_dual_hybrid_gradient.h for more\n",
     "    # details on what the vectors mean if termination_reason is not\n",
     "    # TERMINATION_REASON_OPTIMAL.\n",
-    "    print('Primal solution:', result.primal_solution)\n",
-    "    print('Dual solution:', result.dual_solution)\n",
-    "    print('Reduced costs:', result.reduced_costs)\n",
+    "    print(\"Primal solution:\", result.primal_solution)\n",
+    "    print(\"Dual solution:\", result.dual_solution)\n",
+    "    print(\"Reduced costs:\", result.reduced_costs)\n",
     "\n",
     "    solution_type = solve_log.solution_type\n",
-    "    print('Solution type:', solve_log_pb2.PointType.Name(solution_type))\n",
+    "    print(\"Solution type:\", solve_log_pb2.PointType.Name(solution_type))\n",
     "    for ci in solve_log.solution_stats.convergence_information:\n",
     "        if ci.candidate_type == solution_type:\n",
-    "            print('Primal objective:', ci.primal_objective)\n",
-    "            print('Dual objective:', ci.dual_objective)\n",
+    "            print(\"Primal objective:\", ci.primal_objective)\n",
+    "            print(\"Dual objective:\", ci.dual_objective)\n",
     "\n",
-    "    print('Iterations:', solve_log.iteration_count)\n",
-    "    print('Solve time (sec):', solve_log.solve_time_sec)\n",
+    "    print(\"Iterations:\", solve_log.iteration_count)\n",
+    "    print(\"Solve time (sec):\", solve_log.solve_time_sec)\n",
     "\n",
     "\n",
-    "pywrapinit.CppBridge.InitLogging('simple_pdlp_program.py')\n",
-    "cpp_flags = pywrapinit.CppFlags()\n",
-    "cpp_flags.logtostderr = True\n",
+    "init.CppBridge.init_logging(\"simple_pdlp_program.py\")\n",
+    "cpp_flags = init.CppFlags()\n",
+    "cpp_flags.stderrthreshold = 0\n",
     "cpp_flags.log_prefix = False\n",
-    "pywrapinit.CppBridge.SetFlags(cpp_flags)\n",
+    "init.CppBridge.set_flags(cpp_flags)\n",
     "main()\n",
     "\n"
    ]
diff --git a/examples/notebook/sat/assignment_groups_sat.ipynb b/examples/notebook/sat/assignment_groups_sat.ipynb
index 36f2640bcf..f0bdfb2868 100644
--- a/examples/notebook/sat/assignment_groups_sat.ipynb
+++ b/examples/notebook/sat/assignment_groups_sat.ipynb
@@ -137,7 +137,7 @@
     "    x = {}\n",
     "    for worker in range(num_workers):\n",
     "        for task in range(num_tasks):\n",
-    "            x[worker, task] = model.NewBoolVar(f'x[{worker},{task}]')\n",
+    "            x[worker, task] = model.NewBoolVar(f\"x[{worker},{task}]\")\n",
     "\n",
     "    # Constraints\n",
     "    # Each worker is assigned to at most one task.\n",
@@ -151,12 +151,11 @@
     "    # Create variables for each worker, indicating whether they work on some task.\n",
     "    work = {}\n",
     "    for worker in range(num_workers):\n",
-    "        work[worker] = model.NewBoolVar(f'work[{worker}]')\n",
+    "        work[worker] = model.NewBoolVar(f\"work[{worker}]\")\n",
     "\n",
     "    for worker in range(num_workers):\n",
     "        for task in range(num_tasks):\n",
-    "            model.Add(work[worker] == sum(\n",
-    "                x[worker, task] for task in range(num_tasks)))\n",
+    "            model.Add(work[worker] == sum(x[worker, task] for task in range(num_tasks)))\n",
     "\n",
     "    # Define the allowed groups of worders\n",
     "    model.AddAllowedAssignments([work[0], work[1], work[2], work[3]], group1)\n",
@@ -176,14 +175,16 @@
     "\n",
     "    # Print solution.\n",
     "    if status == cp_model.OPTIMAL or status == cp_model.FEASIBLE:\n",
-    "        print(f'Total cost = {solver.ObjectiveValue()}\\n')\n",
+    "        print(f\"Total cost = {solver.ObjectiveValue()}\\n\")\n",
     "        for worker in range(num_workers):\n",
     "            for task in range(num_tasks):\n",
     "                if solver.BooleanValue(x[worker, task]):\n",
-    "                    print(f'Worker {worker} assigned to task {task}.' +\n",
-    "                          f' Cost = {costs[worker][task]}')\n",
+    "                    print(\n",
+    "                        f\"Worker {worker} assigned to task {task}.\"\n",
+    "                        + f\" Cost = {costs[worker][task]}\"\n",
+    "                    )\n",
     "    else:\n",
-    "        print('No solution found.')\n",
+    "        print(\"No solution found.\")\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/sat/assignment_sat.ipynb b/examples/notebook/sat/assignment_sat.ipynb
index d7217a1ff8..61dc39007b 100644
--- a/examples/notebook/sat/assignment_sat.ipynb
+++ b/examples/notebook/sat/assignment_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Solve a simple assignment problem."
    ]
   },
@@ -82,47 +83,58 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import io\n",
+    "\n",
+    "import pandas as pd\n",
+    "\n",
     "from ortools.sat.python import cp_model\n",
     "\n",
     "\n",
     "def main():\n",
     "    # Data\n",
-    "    costs = [\n",
-    "        [90, 80, 75, 70],\n",
-    "        [35, 85, 55, 65],\n",
-    "        [125, 95, 90, 95],\n",
-    "        [45, 110, 95, 115],\n",
-    "        [50, 100, 90, 100],\n",
-    "    ]\n",
-    "    num_workers = len(costs)\n",
-    "    num_tasks = len(costs[0])\n",
+    "    data_str = \"\"\"\n",
+    "  worker  task  cost\n",
+    "      w1    t1    90\n",
+    "      w1    t2    80\n",
+    "      w1    t3    75\n",
+    "      w1    t4    70\n",
+    "      w2    t1    35\n",
+    "      w2    t2    85\n",
+    "      w2    t3    55\n",
+    "      w2    t4    65\n",
+    "      w3    t1   125\n",
+    "      w3    t2    95\n",
+    "      w3    t3    90\n",
+    "      w3    t4    95\n",
+    "      w4    t1    45\n",
+    "      w4    t2   110\n",
+    "      w4    t3    95\n",
+    "      w4    t4   115\n",
+    "      w5    t1    50\n",
+    "      w5    t2   110\n",
+    "      w5    t3    90\n",
+    "      w5    t4   100\n",
+    "  \"\"\"\n",
+    "\n",
+    "    data = pd.read_table(io.StringIO(data_str), sep=r\"\\s+\")\n",
     "\n",
     "    # Model\n",
     "    model = cp_model.CpModel()\n",
     "\n",
     "    # Variables\n",
-    "    x = []\n",
-    "    for i in range(num_workers):\n",
-    "        t = []\n",
-    "        for j in range(num_tasks):\n",
-    "            t.append(model.NewBoolVar(f'x[{i},{j}]'))\n",
-    "        x.append(t)\n",
+    "    x = model.NewBoolVarSeries(name=\"x\", index=data.index)\n",
     "\n",
     "    # Constraints\n",
     "    # Each worker is assigned to at most one task.\n",
-    "    for i in range(num_workers):\n",
-    "        model.AddAtMostOne(x[i][j] for j in range(num_tasks))\n",
+    "    for unused_name, tasks in data.groupby(\"worker\"):\n",
+    "        model.AddAtMostOne(x[tasks.index])\n",
     "\n",
     "    # Each task is assigned to exactly one worker.\n",
-    "    for j in range(num_tasks):\n",
-    "        model.AddExactlyOne(x[i][j] for i in range(num_workers))\n",
+    "    for unused_name, workers in data.groupby(\"task\"):\n",
+    "        model.AddExactlyOne(x[workers.index])\n",
     "\n",
     "    # Objective\n",
-    "    objective_terms = []\n",
-    "    for i in range(num_workers):\n",
-    "        for j in range(num_tasks):\n",
-    "            objective_terms.append(costs[i][j] * x[i][j])\n",
-    "    model.Minimize(sum(objective_terms))\n",
+    "    model.Minimize(data.cost.dot(x))\n",
     "\n",
     "    # Solve\n",
     "    solver = cp_model.CpSolver()\n",
@@ -130,15 +142,12 @@
     "\n",
     "    # Print solution.\n",
     "    if status == cp_model.OPTIMAL or status == cp_model.FEASIBLE:\n",
-    "        print(f'Total cost = {solver.ObjectiveValue()}')\n",
-    "        print()\n",
-    "        for i in range(num_workers):\n",
-    "            for j in range(num_tasks):\n",
-    "                if solver.BooleanValue(x[i][j]):\n",
-    "                    print(\n",
-    "                        f'Worker {i} assigned to task {j} Cost = {costs[i][j]}')\n",
+    "        print(f\"Total cost = {solver.ObjectiveValue()}\\n\")\n",
+    "        selected = data.loc[solver.BooleanValues(x).loc[lambda x: x].index]\n",
+    "        for unused_index, row in selected.iterrows():\n",
+    "            print(f\"{row.task} assigned to {row.worker} with a cost of {row.cost}\")\n",
     "    else:\n",
-    "        print('No solution found.')\n",
+    "        print(\"No solution found.\")\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/sat/assignment_task_sizes_sat.ipynb b/examples/notebook/sat/assignment_task_sizes_sat.ipynb
index d60e6acfe8..740531d1de 100644
--- a/examples/notebook/sat/assignment_task_sizes_sat.ipynb
+++ b/examples/notebook/sat/assignment_task_sizes_sat.ipynb
@@ -114,14 +114,15 @@
     "    x = {}\n",
     "    for worker in range(num_workers):\n",
     "        for task in range(num_tasks):\n",
-    "            x[worker, task] = model.NewBoolVar(f'x[{worker},{task}]')\n",
+    "            x[worker, task] = model.NewBoolVar(f\"x[{worker},{task}]\")\n",
     "\n",
     "    # Constraints\n",
     "    # Each worker is assigned to at most one task.\n",
     "    for worker in range(num_workers):\n",
     "        model.Add(\n",
-    "            sum(task_sizes[task] * x[worker, task]\n",
-    "                for task in range(num_tasks)) <= total_size_max)\n",
+    "            sum(task_sizes[task] * x[worker, task] for task in range(num_tasks))\n",
+    "            <= total_size_max\n",
+    "        )\n",
     "\n",
     "    # Each task is assigned to exactly one worker.\n",
     "    for task in range(num_tasks):\n",
@@ -140,14 +141,16 @@
     "\n",
     "    # Print solution.\n",
     "    if status == cp_model.OPTIMAL or status == cp_model.FEASIBLE:\n",
-    "        print(f'Total cost = {solver.ObjectiveValue()}\\n')\n",
+    "        print(f\"Total cost = {solver.ObjectiveValue()}\\n\")\n",
     "        for worker in range(num_workers):\n",
     "            for task in range(num_tasks):\n",
     "                if solver.BooleanValue(x[worker, task]):\n",
-    "                    print(f'Worker {worker} assigned to task {task}.' +\n",
-    "                          f' Cost = {costs[worker][task]}')\n",
+    "                    print(\n",
+    "                        f\"Worker {worker} assigned to task {task}.\"\n",
+    "                        + f\" Cost = {costs[worker][task]}\"\n",
+    "                    )\n",
     "    else:\n",
-    "        print('No solution found.')\n",
+    "        print(\"No solution found.\")\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/sat/assignment_teams_sat.ipynb b/examples/notebook/sat/assignment_teams_sat.ipynb
index 46919a4888..5ae5c0fe43 100644
--- a/examples/notebook/sat/assignment_teams_sat.ipynb
+++ b/examples/notebook/sat/assignment_teams_sat.ipynb
@@ -111,7 +111,7 @@
     "    x = {}\n",
     "    for worker in range(num_workers):\n",
     "        for task in range(num_tasks):\n",
-    "            x[worker, task] = model.NewBoolVar(f'x[{worker},{task}]')\n",
+    "            x[worker, task] = model.NewBoolVar(f\"x[{worker},{task}]\")\n",
     "\n",
     "    # Constraints\n",
     "    # Each worker is assigned to at most one task.\n",
@@ -148,14 +148,16 @@
     "\n",
     "    # Print solution.\n",
     "    if status == cp_model.OPTIMAL or status == cp_model.FEASIBLE:\n",
-    "        print(f'Total cost = {solver.ObjectiveValue()}\\n')\n",
+    "        print(f\"Total cost = {solver.ObjectiveValue()}\\n\")\n",
     "        for worker in range(num_workers):\n",
     "            for task in range(num_tasks):\n",
     "                if solver.BooleanValue(x[worker, task]):\n",
-    "                    print(f'Worker {worker} assigned to task {task}.' +\n",
-    "                          f' Cost = {costs[worker][task]}')\n",
+    "                    print(\n",
+    "                        f\"Worker {worker} assigned to task {task}.\"\n",
+    "                        + f\" Cost = {costs[worker][task]}\"\n",
+    "                    )\n",
     "    else:\n",
-    "        print('No solution found.')\n",
+    "        print(\"No solution found.\")\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/sat/assumptions_sample_sat.ipynb b/examples/notebook/sat/assumptions_sample_sat.ipynb
index 6fa9af8386..3651c0ddc1 100644
--- a/examples/notebook/sat/assumptions_sample_sat.ipynb
+++ b/examples/notebook/sat/assumptions_sample_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Code sample that solves a model and gets the infeasibility assumptions."
    ]
   },
@@ -91,12 +92,12 @@
     "    model = cp_model.CpModel()\n",
     "\n",
     "    # Creates the variables.\n",
-    "    x = model.NewIntVar(0, 10, 'x')\n",
-    "    y = model.NewIntVar(0, 10, 'y')\n",
-    "    z = model.NewIntVar(0, 10, 'z')\n",
-    "    a = model.NewBoolVar('a')\n",
-    "    b = model.NewBoolVar('b')\n",
-    "    c = model.NewBoolVar('c')\n",
+    "    x = model.NewIntVar(0, 10, \"x\")\n",
+    "    y = model.NewIntVar(0, 10, \"y\")\n",
+    "    z = model.NewIntVar(0, 10, \"z\")\n",
+    "    a = model.NewBoolVar(\"a\")\n",
+    "    b = model.NewBoolVar(\"b\")\n",
+    "    c = model.NewBoolVar(\"c\")\n",
     "\n",
     "    # Creates the constraints.\n",
     "    model.Add(x > y).OnlyEnforceIf(a)\n",
@@ -111,10 +112,12 @@
     "    status = solver.Solve(model)\n",
     "\n",
     "    # Print solution.\n",
-    "    print(f'Status = {solver.StatusName(status)}')\n",
+    "    print(f\"Status = {solver.StatusName(status)}\")\n",
     "    if status == cp_model.INFEASIBLE:\n",
-    "        print('SufficientAssumptionsForInfeasibility = '\n",
-    "              f'{solver.SufficientAssumptionsForInfeasibility()}')\n",
+    "        print(\n",
+    "            \"SufficientAssumptionsForInfeasibility = \"\n",
+    "            f\"{solver.SufficientAssumptionsForInfeasibility()}\"\n",
+    "        )\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/sat/bin_packing_sat.ipynb b/examples/notebook/sat/bin_packing_sat.ipynb
new file mode 100644
index 0000000000..9c942fff91
--- /dev/null
+++ b/examples/notebook/sat/bin_packing_sat.ipynb
@@ -0,0 +1,193 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "google",
+   "metadata": {},
+   "source": [
+    "##### Copyright 2022 Google LLC."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "apache",
+   "metadata": {},
+   "source": [
+    "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+    "you may not use this file except in compliance with the License.\n",
+    "You may obtain a copy of the License at\n",
+    "\n",
+    "    http://www.apache.org/licenses/LICENSE-2.0\n",
+    "\n",
+    "Unless required by applicable law or agreed to in writing, software\n",
+    "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+    "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+    "See the License for the specific language governing permissions and\n",
+    "limitations under the License.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "basename",
+   "metadata": {},
+   "source": [
+    "# bin_packing_sat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "link",
+   "metadata": {},
+   "source": [
+    "<table align=\"left\">\n",
+    "<td>\n",
+    "<a href=\"https://colab.research.google.com/github/google/or-tools/blob/main/examples/notebook/sat/bin_packing_sat.ipynb\"><img src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/colab_32px.png\"/>Run in Google Colab</a>\n",
+    "</td>\n",
+    "<td>\n",
+    "<a href=\"https://github.com/google/or-tools/blob/main/ortools/sat/samples/bin_packing_sat.py\"><img src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/github_32px.png\"/>View source on GitHub</a>\n",
+    "</td>\n",
+    "</table>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "doc",
+   "metadata": {},
+   "source": [
+    "First, you must install [ortools](https://pypi.org/project/ortools/) package in this colab."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "install",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install ortools"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "description",
+   "metadata": {},
+   "source": [
+    "\n",
+    "Solve a simple bin packing problem using a MIP solver."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "code",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import io\n",
+    "\n",
+    "import pandas as pd\n",
+    "\n",
+    "from ortools.sat.python import cp_model\n",
+    "\n",
+    "\n",
+    "def create_data_model():\n",
+    "    \"\"\"Create the data for the example.\"\"\"\n",
+    "\n",
+    "    items_str = \"\"\"\n",
+    "  item  weight\n",
+    "    i1      48\n",
+    "    i2      30\n",
+    "    i3      19\n",
+    "    i4      36\n",
+    "    i5      36\n",
+    "    i6      27\n",
+    "    i7      42\n",
+    "    i8      42\n",
+    "    i9      36\n",
+    "   i10      24\n",
+    "   i11      30\n",
+    "  \"\"\"\n",
+    "\n",
+    "    bins_str = \"\"\"\n",
+    "  bin  capacity\n",
+    "   b1       100\n",
+    "   b2       100\n",
+    "   b3       100\n",
+    "   b4       100\n",
+    "   b5       100\n",
+    "   b6       100\n",
+    "   b7       100\n",
+    "  \"\"\"\n",
+    "\n",
+    "    items = pd.read_table(io.StringIO(items_str), index_col=0, sep=r\"\\s+\")\n",
+    "    bins = pd.read_table(io.StringIO(bins_str), index_col=0, sep=r\"\\s+\")\n",
+    "    return items, bins\n",
+    "\n",
+    "\n",
+    "def main():\n",
+    "    items, bins = create_data_model()\n",
+    "\n",
+    "    # Create the model.\n",
+    "    model = cp_model.CpModel()\n",
+    "\n",
+    "    # Variables\n",
+    "    # x[i, j] = 1 if item i is packed in bin j.\n",
+    "    items_x_bins = pd.MultiIndex.from_product(\n",
+    "        [items.index, bins.index], names=[\"item\", \"bin\"]\n",
+    "    )\n",
+    "    x = model.NewBoolVarSeries(name=\"x\", index=items_x_bins)\n",
+    "\n",
+    "    # y[j] = 1 if bin j is used.\n",
+    "    y = model.NewBoolVarSeries(name=\"y\", index=bins.index)\n",
+    "\n",
+    "    # Constraints\n",
+    "    # Each item must be in exactly one bin.\n",
+    "    for unused_name, all_copies in x.groupby(\"item\"):\n",
+    "        model.AddExactlyOne(x[all_copies.index])\n",
+    "\n",
+    "    # The amount packed in each bin cannot exceed its capacity.\n",
+    "    for selected_bin in bins.index:\n",
+    "        items_in_bin = x.xs(selected_bin, level=\"bin\")\n",
+    "        model.Add(\n",
+    "            items_in_bin.dot(items.weight)\n",
+    "            <= bins.loc[selected_bin].capacity * y[selected_bin]\n",
+    "        )\n",
+    "\n",
+    "    # Objective: minimize the number of bins used.\n",
+    "    model.Minimize(y.sum())\n",
+    "\n",
+    "    # Create the solver with the CP-SAT backend, and solve the model.\n",
+    "    solver = cp_model.CpSolver()\n",
+    "    status = solver.Solve(model)\n",
+    "\n",
+    "    if status == cp_model.OPTIMAL:\n",
+    "        print(f\"Number of bins used = {solver.ObjectiveValue()}\")\n",
+    "\n",
+    "        x_values = solver.BooleanValues(x)\n",
+    "        y_values = solver.BooleanValues(y)\n",
+    "        active_bins = y_values.loc[lambda x: x].index\n",
+    "\n",
+    "        for b in active_bins:\n",
+    "            print(f\"Bin {b}\")\n",
+    "            items_in_bin = x_values.xs(b, level=\"bin\").loc[lambda x: x].index\n",
+    "            for item in items_in_bin:\n",
+    "                print(f\"  Item {item} - weight {items.loc[item].weight}\")\n",
+    "            print(f\"  Packed items weight: {items.loc[items_in_bin].sum().to_string()}\")\n",
+    "            print()\n",
+    "\n",
+    "        print(f\"Total packed weight: {items.weight.sum()}\")\n",
+    "        print()\n",
+    "        print(f\"Time = {solver.WallTime()} seconds\")\n",
+    "    else:\n",
+    "        print(\"The problem does not have an optimal solution.\")\n",
+    "\n",
+    "\n",
+    "main()\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/notebook/sat/binpacking_problem_sat.ipynb b/examples/notebook/sat/binpacking_problem_sat.ipynb
index e32355b77c..6bb71b2ba3 100644
--- a/examples/notebook/sat/binpacking_problem_sat.ipynb
+++ b/examples/notebook/sat/binpacking_problem_sat.ipynb
@@ -72,7 +72,9 @@
    "id": "description",
    "metadata": {},
    "source": [
-    "Solves a binpacking problem using the CP-SAT solver.\n"
+    "\n",
+    "Solves a binpacking problem using the CP-SAT solver.\n",
+    "\n"
    ]
   },
   {
@@ -105,13 +107,13 @@
     "    for i in all_items:\n",
     "        num_copies = items[i][1]\n",
     "        for b in all_bins:\n",
-    "            x[(i, b)] = model.NewIntVar(0, num_copies, 'x_%i_%i' % (i, b))\n",
+    "            x[(i, b)] = model.NewIntVar(0, num_copies, f\"x[{i},{b}]\")\n",
     "\n",
     "    # Load variables.\n",
-    "    load = [model.NewIntVar(0, bin_capacity, 'load_%i' % b) for b in all_bins]\n",
+    "    load = [model.NewIntVar(0, bin_capacity, f\"load[{b}]\") for b in all_bins]\n",
     "\n",
     "    # Slack variables.\n",
-    "    slacks = [model.NewBoolVar('slack_%i' % b) for b in all_bins]\n",
+    "    slacks = [model.NewBoolVar(f\"slack[{b}]\") for b in all_bins]\n",
     "\n",
     "    # Links load and x.\n",
     "    for b in all_bins:\n",
@@ -135,13 +137,13 @@
     "    # Solves and prints out the solution.\n",
     "    solver = cp_model.CpSolver()\n",
     "    status = solver.Solve(model)\n",
-    "    print('Solve status: %s' % solver.StatusName(status))\n",
+    "    print(f\"Solve status: {solver.StatusName(status)}\")\n",
     "    if status == cp_model.OPTIMAL:\n",
-    "        print('Optimal objective value: %i' % solver.ObjectiveValue())\n",
-    "    print('Statistics')\n",
-    "    print('  - conflicts : %i' % solver.NumConflicts())\n",
-    "    print('  - branches  : %i' % solver.NumBranches())\n",
-    "    print('  - wall time : %f s' % solver.WallTime())\n",
+    "        print(f\"Optimal objective value: {solver.ObjectiveValue()}\")\n",
+    "    print(\"Statistics\")\n",
+    "    print(f\"  - conflicts : {solver.NumConflicts()}\")\n",
+    "    print(f\"  - branches  : {solver.NumBranches()}\")\n",
+    "    print(f\"  - wall time : {solver.WallTime()}s\")\n",
     "\n",
     "\n",
     "BinpackingProblemSat()\n",
diff --git a/examples/notebook/sat/bool_or_sample_sat.ipynb b/examples/notebook/sat/bool_or_sample_sat.ipynb
index d75b962489..ac4bd4d5d2 100644
--- a/examples/notebook/sat/bool_or_sample_sat.ipynb
+++ b/examples/notebook/sat/bool_or_sample_sat.ipynb
@@ -72,7 +72,9 @@
    "id": "description",
    "metadata": {},
    "source": [
-    "Code sample to demonstrates a simple Boolean constraint.\n"
+    "\n",
+    "Code sample to demonstrates a simple Boolean constraint.\n",
+    "\n"
    ]
   },
   {
@@ -88,8 +90,8 @@
     "def BoolOrSampleSat():\n",
     "    model = cp_model.CpModel()\n",
     "\n",
-    "    x = model.NewBoolVar('x')\n",
-    "    y = model.NewBoolVar('y')\n",
+    "    x = model.NewBoolVar(\"x\")\n",
+    "    y = model.NewBoolVar(\"y\")\n",
     "\n",
     "    model.AddBoolOr([x, y.Not()])\n",
     "\n",
diff --git a/examples/notebook/sat/boolean_product_sample_sat.ipynb b/examples/notebook/sat/boolean_product_sample_sat.ipynb
index 85b7b73b1f..5f175225d0 100644
--- a/examples/notebook/sat/boolean_product_sample_sat.ipynb
+++ b/examples/notebook/sat/boolean_product_sample_sat.ipynb
@@ -72,7 +72,9 @@
    "id": "description",
    "metadata": {},
    "source": [
-    "Code sample that encodes the product of two Boolean variables.\n"
+    "\n",
+    "Code sample that encodes the product of two Boolean variables.\n",
+    "\n"
    ]
   },
   {
@@ -88,12 +90,12 @@
     "def BooleanProductSampleSat():\n",
     "    \"\"\"Encoding of the product of two Boolean variables.\n",
     "\n",
-    "  p == x * y, which is the same as p <=> x and y\n",
-    "  \"\"\"\n",
+    "    p == x * y, which is the same as p <=> x and y\n",
+    "    \"\"\"\n",
     "    model = cp_model.CpModel()\n",
-    "    x = model.NewBoolVar('x')\n",
-    "    y = model.NewBoolVar('y')\n",
-    "    p = model.NewBoolVar('p')\n",
+    "    x = model.NewBoolVar(\"x\")\n",
+    "    y = model.NewBoolVar(\"y\")\n",
+    "    p = model.NewBoolVar(\"p\")\n",
     "\n",
     "    # x and y implies p, rewrite as not(x and y) or p.\n",
     "    model.AddBoolOr(x.Not(), y.Not(), p)\n",
diff --git a/examples/notebook/sat/channeling_sample_sat.ipynb b/examples/notebook/sat/channeling_sample_sat.ipynb
index 77b73816e1..6e8ff9d0ed 100644
--- a/examples/notebook/sat/channeling_sample_sat.ipynb
+++ b/examples/notebook/sat/channeling_sample_sat.ipynb
@@ -72,7 +72,9 @@
    "id": "description",
    "metadata": {},
    "source": [
-    "Link integer constraints together.\n"
+    "\n",
+    "Link integer constraints together.\n",
+    "\n"
    ]
   },
   {
@@ -96,7 +98,7 @@
     "    def on_solution_callback(self):\n",
     "        self.__solution_count += 1\n",
     "        for v in self.__variables:\n",
-    "            print('%s=%i' % (v, self.Value(v)), end=' ')\n",
+    "            print(f\"{v}={self.Value(v)}\", end=\" \")\n",
     "        print()\n",
     "\n",
     "    def solution_count(self):\n",
@@ -110,11 +112,11 @@
     "    model = cp_model.CpModel()\n",
     "\n",
     "    # Declare our two primary variables.\n",
-    "    x = model.NewIntVar(0, 10, 'x')\n",
-    "    y = model.NewIntVar(0, 10, 'y')\n",
+    "    x = model.NewIntVar(0, 10, \"x\")\n",
+    "    y = model.NewIntVar(0, 10, \"y\")\n",
     "\n",
     "    # Declare our intermediate boolean variable.\n",
-    "    b = model.NewBoolVar('b')\n",
+    "    b = model.NewBoolVar(\"b\")\n",
     "\n",
     "    # Implement b == (x >= 5).\n",
     "    model.Add(x >= 5).OnlyEnforceIf(b)\n",
@@ -127,8 +129,7 @@
     "    model.Add(y == 0).OnlyEnforceIf(b.Not())\n",
     "\n",
     "    # Search for x values in increasing order.\n",
-    "    model.AddDecisionStrategy([x], cp_model.CHOOSE_FIRST,\n",
-    "                              cp_model.SELECT_MIN_VALUE)\n",
+    "    model.AddDecisionStrategy([x], cp_model.CHOOSE_FIRST, cp_model.SELECT_MIN_VALUE)\n",
     "\n",
     "    # Create a solver and solve with a fixed search.\n",
     "    solver = cp_model.CpSolver()\n",
diff --git a/examples/notebook/sat/copy_model_sample_sat.ipynb b/examples/notebook/sat/copy_model_sample_sat.ipynb
index cf7e4c5f77..bb699df6dc 100644
--- a/examples/notebook/sat/copy_model_sample_sat.ipynb
+++ b/examples/notebook/sat/copy_model_sample_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Showcases deep copying of a model.\n"
    ]
   },
@@ -92,9 +93,9 @@
     "\n",
     "    # Creates the variables.\n",
     "    num_vals = 3\n",
-    "    x = model.NewIntVar(0, num_vals - 1, 'x')\n",
-    "    y = model.NewIntVar(0, num_vals - 1, 'y')\n",
-    "    z = model.NewIntVar(0, num_vals - 1, 'z')\n",
+    "    x = model.NewIntVar(0, num_vals - 1, \"x\")\n",
+    "    y = model.NewIntVar(0, num_vals - 1, \"y\")\n",
+    "    z = model.NewIntVar(0, num_vals - 1, \"z\")\n",
     "\n",
     "    # Creates the constraints.\n",
     "    model.Add(x != y)\n",
@@ -106,8 +107,7 @@
     "    status = solver.Solve(model)\n",
     "\n",
     "    if status == cp_model.OPTIMAL:\n",
-    "        print('Optimal value of the original model: {}'.format(\n",
-    "            solver.ObjectiveValue()))\n",
+    "        print(\"Optimal value of the original model: {}\".format(solver.ObjectiveValue()))\n",
     "\n",
     "    # Copy the model.\n",
     "    copy = cp_model.CpModel()\n",
@@ -120,8 +120,7 @@
     "    status = solver.Solve(copy)\n",
     "\n",
     "    if status == cp_model.OPTIMAL:\n",
-    "        print('Optimal value of the modified model: {}'.format(\n",
-    "            solver.ObjectiveValue()))\n",
+    "        print(\"Optimal value of the modified model: {}\".format(solver.ObjectiveValue()))\n",
     "\n",
     "\n",
     "CopyModelSat()\n",
diff --git a/examples/notebook/sat/cp_is_fun_sat.ipynb b/examples/notebook/sat/cp_is_fun_sat.ipynb
index 71453b7a7c..93ec40089e 100644
--- a/examples/notebook/sat/cp_is_fun_sat.ipynb
+++ b/examples/notebook/sat/cp_is_fun_sat.ipynb
@@ -102,7 +102,7 @@
     "    def on_solution_callback(self):\n",
     "        self.__solution_count += 1\n",
     "        for v in self.__variables:\n",
-    "            print('%s=%i' % (v, self.Value(v)), end=' ')\n",
+    "            print(f\"{v}={self.Value(v)}\", end=\" \")\n",
     "        print()\n",
     "\n",
     "    def solution_count(self):\n",
@@ -116,16 +116,16 @@
     "\n",
     "    base = 10\n",
     "\n",
-    "    c = model.NewIntVar(1, base - 1, 'C')\n",
-    "    p = model.NewIntVar(0, base - 1, 'P')\n",
-    "    i = model.NewIntVar(1, base - 1, 'I')\n",
-    "    s = model.NewIntVar(0, base - 1, 'S')\n",
-    "    f = model.NewIntVar(1, base - 1, 'F')\n",
-    "    u = model.NewIntVar(0, base - 1, 'U')\n",
-    "    n = model.NewIntVar(0, base - 1, 'N')\n",
-    "    t = model.NewIntVar(1, base - 1, 'T')\n",
-    "    r = model.NewIntVar(0, base - 1, 'R')\n",
-    "    e = model.NewIntVar(0, base - 1, 'E')\n",
+    "    c = model.NewIntVar(1, base - 1, \"C\")\n",
+    "    p = model.NewIntVar(0, base - 1, \"P\")\n",
+    "    i = model.NewIntVar(1, base - 1, \"I\")\n",
+    "    s = model.NewIntVar(0, base - 1, \"S\")\n",
+    "    f = model.NewIntVar(1, base - 1, \"F\")\n",
+    "    u = model.NewIntVar(0, base - 1, \"U\")\n",
+    "    n = model.NewIntVar(0, base - 1, \"N\")\n",
+    "    t = model.NewIntVar(1, base - 1, \"T\")\n",
+    "    r = model.NewIntVar(0, base - 1, \"R\")\n",
+    "    e = model.NewIntVar(0, base - 1, \"E\")\n",
     "\n",
     "    # We need to group variables in a list to use the constraint AllDifferent.\n",
     "    letters = [c, p, i, s, f, u, n, t, r, e]\n",
@@ -137,8 +137,10 @@
     "    model.AddAllDifferent(letters)\n",
     "\n",
     "    # CP + IS + FUN = TRUE\n",
-    "    model.Add(c * base + p + i * base + s + f * base * base + u * base +\n",
-    "              n == t * base * base * base + r * base * base + u * base + e)\n",
+    "    model.Add(\n",
+    "        c * base + p + i * base + s + f * base * base + u * base + n\n",
+    "        == t * base * base * base + r * base * base + u * base + e\n",
+    "    )\n",
     "\n",
     "    # Creates a solver and solves the model.\n",
     "    solver = cp_model.CpSolver()\n",
@@ -149,12 +151,12 @@
     "    status = solver.Solve(model, solution_printer)\n",
     "\n",
     "    # Statistics.\n",
-    "    print('\\nStatistics')\n",
-    "    print(f'  status   : {solver.StatusName(status)}')\n",
-    "    print(f'  conflicts: {solver.NumConflicts()}')\n",
-    "    print(f'  branches : {solver.NumBranches()}')\n",
-    "    print(f'  wall time: {solver.WallTime()} s')\n",
-    "    print(f'  sol found: {solution_printer.solution_count()}')\n",
+    "    print(\"\\nStatistics\")\n",
+    "    print(f\"  status   : {solver.StatusName(status)}\")\n",
+    "    print(f\"  conflicts: {solver.NumConflicts()}\")\n",
+    "    print(f\"  branches : {solver.NumBranches()}\")\n",
+    "    print(f\"  wall time: {solver.WallTime()} s\")\n",
+    "    print(f\"  sol found: {solution_printer.solution_count()}\")\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/sat/cp_sat_example.ipynb b/examples/notebook/sat/cp_sat_example.ipynb
index f16ebe7888..6efc105794 100644
--- a/examples/notebook/sat/cp_sat_example.ipynb
+++ b/examples/notebook/sat/cp_sat_example.ipynb
@@ -93,9 +93,9 @@
     "\n",
     "    # Creates the variables.\n",
     "    var_upper_bound = max(50, 45, 37)\n",
-    "    x = model.NewIntVar(0, var_upper_bound, 'x')\n",
-    "    y = model.NewIntVar(0, var_upper_bound, 'y')\n",
-    "    z = model.NewIntVar(0, var_upper_bound, 'z')\n",
+    "    x = model.NewIntVar(0, var_upper_bound, \"x\")\n",
+    "    y = model.NewIntVar(0, var_upper_bound, \"y\")\n",
+    "    z = model.NewIntVar(0, var_upper_bound, \"z\")\n",
     "\n",
     "    # Creates the constraints.\n",
     "    model.Add(2 * x + 7 * y + 3 * z <= 50)\n",
@@ -109,19 +109,19 @@
     "    status = solver.Solve(model)\n",
     "\n",
     "    if status == cp_model.OPTIMAL or status == cp_model.FEASIBLE:\n",
-    "        print(f'Maximum of objective function: {solver.ObjectiveValue()}\\n')\n",
-    "        print(f'x = {solver.Value(x)}')\n",
-    "        print(f'y = {solver.Value(y)}')\n",
-    "        print(f'z = {solver.Value(z)}')\n",
+    "        print(f\"Maximum of objective function: {solver.ObjectiveValue()}\\n\")\n",
+    "        print(f\"x = {solver.Value(x)}\")\n",
+    "        print(f\"y = {solver.Value(y)}\")\n",
+    "        print(f\"z = {solver.Value(z)}\")\n",
     "    else:\n",
-    "        print('No solution found.')\n",
+    "        print(\"No solution found.\")\n",
     "\n",
     "    # Statistics.\n",
-    "    print('\\nStatistics')\n",
-    "    print(f'  status   : {solver.StatusName(status)}')\n",
-    "    print(f'  conflicts: {solver.NumConflicts()}')\n",
-    "    print(f'  branches : {solver.NumBranches()}')\n",
-    "    print(f'  wall time: {solver.WallTime()} s')\n",
+    "    print(\"\\nStatistics\")\n",
+    "    print(f\"  status   : {solver.StatusName(status)}\")\n",
+    "    print(f\"  conflicts: {solver.NumConflicts()}\")\n",
+    "    print(f\"  branches : {solver.NumBranches()}\")\n",
+    "    print(f\"  wall time: {solver.WallTime()} s\")\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/sat/earliness_tardiness_cost_sample_sat.ipynb b/examples/notebook/sat/earliness_tardiness_cost_sample_sat.ipynb
index 999c98e8b2..8434d96f2e 100644
--- a/examples/notebook/sat/earliness_tardiness_cost_sample_sat.ipynb
+++ b/examples/notebook/sat/earliness_tardiness_cost_sample_sat.ipynb
@@ -72,7 +72,9 @@
    "id": "description",
    "metadata": {},
    "source": [
-    "Encodes an convex piecewise linear function.\n"
+    "\n",
+    "Encodes a convex piecewise linear function.\n",
+    "\n"
    ]
   },
   {
@@ -96,7 +98,7 @@
     "    def on_solution_callback(self):\n",
     "        self.__solution_count += 1\n",
     "        for v in self.__variables:\n",
-    "            print('%s=%i' % (v, self.Value(v)), end=' ')\n",
+    "            print(f\"{v}={self.Value(v)}\", end=\" \")\n",
     "        print()\n",
     "\n",
     "    def solution_count(self):\n",
@@ -115,7 +117,7 @@
     "    model = cp_model.CpModel()\n",
     "\n",
     "    # Declare our primary variable.\n",
-    "    x = model.NewIntVar(0, 20, 'x')\n",
+    "    x = model.NewIntVar(0, 20, \"x\")\n",
     "\n",
     "    # Create the expression variable and implement the piecewise linear function.\n",
     "    #\n",
@@ -124,25 +126,24 @@
     "    #   ed    ld\n",
     "    #\n",
     "    large_constant = 1000\n",
-    "    expr = model.NewIntVar(0, large_constant, 'expr')\n",
+    "    expr = model.NewIntVar(0, large_constant, \"expr\")\n",
     "\n",
     "    # First segment.\n",
-    "    s1 = model.NewIntVar(-large_constant, large_constant, 's1')\n",
+    "    s1 = model.NewIntVar(-large_constant, large_constant, \"s1\")\n",
     "    model.Add(s1 == earliness_cost * (earliness_date - x))\n",
     "\n",
     "    # Second segment.\n",
     "    s2 = 0\n",
     "\n",
     "    # Third segment.\n",
-    "    s3 = model.NewIntVar(-large_constant, large_constant, 's3')\n",
+    "    s3 = model.NewIntVar(-large_constant, large_constant, \"s3\")\n",
     "    model.Add(s3 == lateness_cost * (x - lateness_date))\n",
     "\n",
     "    # Link together expr and x through s1, s2, and s3.\n",
     "    model.AddMaxEquality(expr, [s1, s2, s3])\n",
     "\n",
     "    # Search for x values in increasing order.\n",
-    "    model.AddDecisionStrategy([x], cp_model.CHOOSE_FIRST,\n",
-    "                              cp_model.SELECT_MIN_VALUE)\n",
+    "    model.AddDecisionStrategy([x], cp_model.CHOOSE_FIRST, cp_model.SELECT_MIN_VALUE)\n",
     "\n",
     "    # Create a solver and solve with a fixed search.\n",
     "    solver = cp_model.CpSolver()\n",
diff --git a/examples/notebook/sat/interval_sample_sat.ipynb b/examples/notebook/sat/interval_sample_sat.ipynb
index 358ea96d6e..dc06fe8269 100644
--- a/examples/notebook/sat/interval_sample_sat.ipynb
+++ b/examples/notebook/sat/interval_sample_sat.ipynb
@@ -72,7 +72,9 @@
    "id": "description",
    "metadata": {},
    "source": [
-    "Code sample to demonstrates how to build an interval.\n"
+    "\n",
+    "Code sample to demonstrates how to build an interval.\n",
+    "\n"
    ]
   },
   {
@@ -91,23 +93,23 @@
     "    horizon = 100\n",
     "\n",
     "    # An interval can be created from three affine expressions.\n",
-    "    start_var = model.NewIntVar(0, horizon, 'start')\n",
+    "    start_var = model.NewIntVar(0, horizon, \"start\")\n",
     "    duration = 10  # Python cp/sat code accept integer variables or constants.\n",
-    "    end_var = model.NewIntVar(0, horizon, 'end')\n",
-    "    interval_var = model.NewIntervalVar(start_var, duration, end_var + 2,\n",
-    "                                        'interval')\n",
+    "    end_var = model.NewIntVar(0, horizon, \"end\")\n",
+    "    interval_var = model.NewIntervalVar(start_var, duration, end_var + 2, \"interval\")\n",
     "\n",
-    "    print(f'interval = {repr(interval_var)}')\n",
+    "    print(f\"interval = {repr(interval_var)}\")\n",
     "\n",
     "    # If the size is fixed, a simpler version uses the start expression and the\n",
     "    # size.\n",
     "    fixed_size_interval_var = model.NewFixedSizeIntervalVar(\n",
-    "        start_var, 10, 'fixed_size_interval_var')\n",
-    "    print(f'fixed_size_interval_var = {repr(fixed_size_interval_var)}')\n",
+    "        start_var, 10, \"fixed_size_interval_var\"\n",
+    "    )\n",
+    "    print(f\"fixed_size_interval_var = {repr(fixed_size_interval_var)}\")\n",
     "\n",
     "    # A fixed interval can be created using the same API.\n",
-    "    fixed_interval = model.NewFixedSizeIntervalVar(5, 10, 'fixed_interval')\n",
-    "    print(f'fixed_interval = {repr(fixed_interval)}')\n",
+    "    fixed_interval = model.NewFixedSizeIntervalVar(5, 10, \"fixed_interval\")\n",
+    "    print(f\"fixed_interval = {repr(fixed_interval)}\")\n",
     "\n",
     "\n",
     "IntervalSampleSat()\n",
diff --git a/examples/notebook/sat/literal_sample_sat.ipynb b/examples/notebook/sat/literal_sample_sat.ipynb
index c6740c6258..148338b52d 100644
--- a/examples/notebook/sat/literal_sample_sat.ipynb
+++ b/examples/notebook/sat/literal_sample_sat.ipynb
@@ -72,7 +72,9 @@
    "id": "description",
    "metadata": {},
    "source": [
-    "Code sample to demonstrate Boolean variable and literals.\n"
+    "\n",
+    "Code sample to demonstrate Boolean variable and literals.\n",
+    "\n"
    ]
   },
   {
@@ -87,7 +89,7 @@
     "\n",
     "def LiteralSampleSat():\n",
     "    model = cp_model.CpModel()\n",
-    "    x = model.NewBoolVar('x')\n",
+    "    x = model.NewBoolVar(\"x\")\n",
     "    not_x = x.Not()\n",
     "    print(x)\n",
     "    print(not_x)\n",
diff --git a/examples/notebook/sat/minimal_jobshop_sat.ipynb b/examples/notebook/sat/minimal_jobshop_sat.ipynb
index 63ee095f97..39624b4aa0 100644
--- a/examples/notebook/sat/minimal_jobshop_sat.ipynb
+++ b/examples/notebook/sat/minimal_jobshop_sat.ipynb
@@ -93,7 +93,7 @@
     "    jobs_data = [  # task = (machine_id, processing_time).\n",
     "        [(0, 3), (1, 2), (2, 2)],  # Job0\n",
     "        [(0, 2), (2, 1), (1, 4)],  # Job1\n",
-    "        [(1, 4), (2, 3)]  # Job2\n",
+    "        [(1, 4), (2, 3)],  # Job2\n",
     "    ]\n",
     "\n",
     "    machines_count = 1 + max(task[0] for job in jobs_data for task in job)\n",
@@ -105,10 +105,11 @@
     "    model = cp_model.CpModel()\n",
     "\n",
     "    # Named tuple to store information about created variables.\n",
-    "    task_type = collections.namedtuple('task_type', 'start end interval')\n",
+    "    task_type = collections.namedtuple(\"task_type\", \"start end interval\")\n",
     "    # Named tuple to manipulate solution information.\n",
-    "    assigned_task_type = collections.namedtuple('assigned_task_type',\n",
-    "                                                'start job index duration')\n",
+    "    assigned_task_type = collections.namedtuple(\n",
+    "        \"assigned_task_type\", \"start job index duration\"\n",
+    "    )\n",
     "\n",
     "    # Creates job intervals and add to the corresponding machine lists.\n",
     "    all_tasks = {}\n",
@@ -116,16 +117,16 @@
     "\n",
     "    for job_id, job in enumerate(jobs_data):\n",
     "        for task_id, task in enumerate(job):\n",
-    "            machine = task[0]\n",
-    "            duration = task[1]\n",
-    "            suffix = '_%i_%i' % (job_id, task_id)\n",
-    "            start_var = model.NewIntVar(0, horizon, 'start' + suffix)\n",
-    "            end_var = model.NewIntVar(0, horizon, 'end' + suffix)\n",
-    "            interval_var = model.NewIntervalVar(start_var, duration, end_var,\n",
-    "                                                'interval' + suffix)\n",
-    "            all_tasks[job_id, task_id] = task_type(start=start_var,\n",
-    "                                                   end=end_var,\n",
-    "                                                   interval=interval_var)\n",
+    "            machine, duration = task\n",
+    "            suffix = f\"_{job_id}_{task_id}\"\n",
+    "            start_var = model.NewIntVar(0, horizon, \"start\" + suffix)\n",
+    "            end_var = model.NewIntVar(0, horizon, \"end\" + suffix)\n",
+    "            interval_var = model.NewIntervalVar(\n",
+    "                start_var, duration, end_var, \"interval\" + suffix\n",
+    "            )\n",
+    "            all_tasks[job_id, task_id] = task_type(\n",
+    "                start=start_var, end=end_var, interval=interval_var\n",
+    "            )\n",
     "            machine_to_intervals[machine].append(interval_var)\n",
     "\n",
     "    # Create and add disjunctive constraints.\n",
@@ -135,15 +136,16 @@
     "    # Precedences inside a job.\n",
     "    for job_id, job in enumerate(jobs_data):\n",
     "        for task_id in range(len(job) - 1):\n",
-    "            model.Add(all_tasks[job_id, task_id +\n",
-    "                                1].start >= all_tasks[job_id, task_id].end)\n",
+    "            model.Add(\n",
+    "                all_tasks[job_id, task_id + 1].start >= all_tasks[job_id, task_id].end\n",
+    "            )\n",
     "\n",
     "    # Makespan objective.\n",
-    "    obj_var = model.NewIntVar(0, horizon, 'makespan')\n",
-    "    model.AddMaxEquality(obj_var, [\n",
-    "        all_tasks[job_id, len(job) - 1].end\n",
-    "        for job_id, job in enumerate(jobs_data)\n",
-    "    ])\n",
+    "    obj_var = model.NewIntVar(0, horizon, \"makespan\")\n",
+    "    model.AddMaxEquality(\n",
+    "        obj_var,\n",
+    "        [all_tasks[job_id, len(job) - 1].end for job_id, job in enumerate(jobs_data)],\n",
+    "    )\n",
     "    model.Minimize(obj_var)\n",
     "\n",
     "    # Creates the solver and solve.\n",
@@ -151,55 +153,56 @@
     "    status = solver.Solve(model)\n",
     "\n",
     "    if status == cp_model.OPTIMAL or status == cp_model.FEASIBLE:\n",
-    "        print('Solution:')\n",
+    "        print(\"Solution:\")\n",
     "        # Create one list of assigned tasks per machine.\n",
     "        assigned_jobs = collections.defaultdict(list)\n",
     "        for job_id, job in enumerate(jobs_data):\n",
     "            for task_id, task in enumerate(job):\n",
     "                machine = task[0]\n",
     "                assigned_jobs[machine].append(\n",
-    "                    assigned_task_type(start=solver.Value(\n",
-    "                        all_tasks[job_id, task_id].start),\n",
-    "                                       job=job_id,\n",
-    "                                       index=task_id,\n",
-    "                                       duration=task[1]))\n",
+    "                    assigned_task_type(\n",
+    "                        start=solver.Value(all_tasks[job_id, task_id].start),\n",
+    "                        job=job_id,\n",
+    "                        index=task_id,\n",
+    "                        duration=task[1],\n",
+    "                    )\n",
+    "                )\n",
     "\n",
     "        # Create per machine output lines.\n",
-    "        output = ''\n",
+    "        output = \"\"\n",
     "        for machine in all_machines:\n",
     "            # Sort by starting time.\n",
     "            assigned_jobs[machine].sort()\n",
-    "            sol_line_tasks = 'Machine ' + str(machine) + ': '\n",
-    "            sol_line = '           '\n",
+    "            sol_line_tasks = \"Machine \" + str(machine) + \": \"\n",
+    "            sol_line = \"           \"\n",
     "\n",
     "            for assigned_task in assigned_jobs[machine]:\n",
-    "                name = 'job_%i_task_%i' % (assigned_task.job,\n",
-    "                                           assigned_task.index)\n",
+    "                name = f\"job_{assigned_task.job}_task_{assigned_task.index}\"\n",
     "                # Add spaces to output to align columns.\n",
-    "                sol_line_tasks += '%-15s' % name\n",
+    "                sol_line_tasks += f\"{name:15}\"\n",
     "\n",
     "                start = assigned_task.start\n",
     "                duration = assigned_task.duration\n",
-    "                sol_tmp = '[%i,%i]' % (start, start + duration)\n",
+    "                sol_tmp = f\"[{start},{start + duration}]\"\n",
     "                # Add spaces to output to align columns.\n",
-    "                sol_line += '%-15s' % sol_tmp\n",
+    "                sol_line += f\"{sol_tmp:15}\"\n",
     "\n",
-    "            sol_line += '\\n'\n",
-    "            sol_line_tasks += '\\n'\n",
+    "            sol_line += \"\\n\"\n",
+    "            sol_line_tasks += \"\\n\"\n",
     "            output += sol_line_tasks\n",
     "            output += sol_line\n",
     "\n",
     "        # Finally print the solution found.\n",
-    "        print(f'Optimal Schedule Length: {solver.ObjectiveValue()}')\n",
+    "        print(f\"Optimal Schedule Length: {solver.ObjectiveValue()}\")\n",
     "        print(output)\n",
     "    else:\n",
-    "        print('No solution found.')\n",
+    "        print(\"No solution found.\")\n",
     "\n",
     "    # Statistics.\n",
-    "    print('\\nStatistics')\n",
-    "    print('  - conflicts: %i' % solver.NumConflicts())\n",
-    "    print('  - branches : %i' % solver.NumBranches())\n",
-    "    print('  - wall time: %f s' % solver.WallTime())\n",
+    "    print(\"\\nStatistics\")\n",
+    "    print(f\"  - conflicts: {solver.NumConflicts()}\")\n",
+    "    print(f\"  - branches : {solver.NumBranches()}\")\n",
+    "    print(f\"  - wall time: {solver.WallTime()}s\")\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/sat/multiple_knapsack_sat.ipynb b/examples/notebook/sat/multiple_knapsack_sat.ipynb
index 3efba48f50..b4984254b2 100644
--- a/examples/notebook/sat/multiple_knapsack_sat.ipynb
+++ b/examples/notebook/sat/multiple_knapsack_sat.ipynb
@@ -88,72 +88,68 @@
     "\n",
     "def main():\n",
     "    data = {}\n",
-    "    data['weights'] = [\n",
-    "        48, 30, 42, 36, 36, 48, 42, 42, 36, 24, 30, 30, 42, 36, 36\n",
-    "    ]\n",
-    "    data['values'] = [\n",
-    "        10, 30, 25, 50, 35, 30, 15, 40, 30, 35, 45, 10, 20, 30, 25\n",
-    "    ]\n",
-    "    assert len(data['weights']) == len(data['values'])\n",
-    "    data['num_items'] = len(data['weights'])\n",
-    "    data['all_items'] = range(data['num_items'])\n",
+    "    data[\"weights\"] = [48, 30, 42, 36, 36, 48, 42, 42, 36, 24, 30, 30, 42, 36, 36]\n",
+    "    data[\"values\"] = [10, 30, 25, 50, 35, 30, 15, 40, 30, 35, 45, 10, 20, 30, 25]\n",
+    "    assert len(data[\"weights\"]) == len(data[\"values\"])\n",
+    "    data[\"num_items\"] = len(data[\"weights\"])\n",
+    "    data[\"all_items\"] = range(data[\"num_items\"])\n",
     "\n",
-    "    data['bin_capacities'] = [100, 100, 100, 100, 100]\n",
-    "    data['num_bins'] = len(data['bin_capacities'])\n",
-    "    data['all_bins'] = range(data['num_bins'])\n",
+    "    data[\"bin_capacities\"] = [100, 100, 100, 100, 100]\n",
+    "    data[\"num_bins\"] = len(data[\"bin_capacities\"])\n",
+    "    data[\"all_bins\"] = range(data[\"num_bins\"])\n",
     "\n",
     "    model = cp_model.CpModel()\n",
     "\n",
     "    # Variables.\n",
     "    # x[i, b] = 1 if item i is packed in bin b.\n",
     "    x = {}\n",
-    "    for i in data['all_items']:\n",
-    "        for b in data['all_bins']:\n",
-    "            x[i, b] = model.NewBoolVar(f'x_{i}_{b}')\n",
+    "    for i in data[\"all_items\"]:\n",
+    "        for b in data[\"all_bins\"]:\n",
+    "            x[i, b] = model.NewBoolVar(f\"x_{i}_{b}\")\n",
     "\n",
     "    # Constraints.\n",
     "    # Each item is assigned to at most one bin.\n",
-    "    for i in data['all_items']:\n",
-    "        model.AddAtMostOne(x[i, b] for b in data['all_bins'])\n",
+    "    for i in data[\"all_items\"]:\n",
+    "        model.AddAtMostOne(x[i, b] for b in data[\"all_bins\"])\n",
     "\n",
     "    # The amount packed in each bin cannot exceed its capacity.\n",
-    "    for b in data['all_bins']:\n",
+    "    for b in data[\"all_bins\"]:\n",
     "        model.Add(\n",
-    "            sum(x[i, b] * data['weights'][i]\n",
-    "                for i in data['all_items']) <= data['bin_capacities'][b])\n",
+    "            sum(x[i, b] * data[\"weights\"][i] for i in data[\"all_items\"])\n",
+    "            <= data[\"bin_capacities\"][b]\n",
+    "        )\n",
     "\n",
     "    # Objective.\n",
     "    # Maximize total value of packed items.\n",
     "    objective = []\n",
-    "    for i in data['all_items']:\n",
-    "        for b in data['all_bins']:\n",
-    "            objective.append(\n",
-    "                cp_model.LinearExpr.Term(x[i, b], data['values'][i]))\n",
+    "    for i in data[\"all_items\"]:\n",
+    "        for b in data[\"all_bins\"]:\n",
+    "            objective.append(cp_model.LinearExpr.Term(x[i, b], data[\"values\"][i]))\n",
     "    model.Maximize(cp_model.LinearExpr.Sum(objective))\n",
     "\n",
     "    solver = cp_model.CpSolver()\n",
     "    status = solver.Solve(model)\n",
     "\n",
     "    if status == cp_model.OPTIMAL:\n",
-    "        print(f'Total packed value: {solver.ObjectiveValue()}')\n",
+    "        print(f\"Total packed value: {solver.ObjectiveValue()}\")\n",
     "        total_weight = 0\n",
-    "        for b in data['all_bins']:\n",
-    "            print(f'Bin {b}')\n",
+    "        for b in data[\"all_bins\"]:\n",
+    "            print(f\"Bin {b}\")\n",
     "            bin_weight = 0\n",
     "            bin_value = 0\n",
-    "            for i in data['all_items']:\n",
+    "            for i in data[\"all_items\"]:\n",
     "                if solver.Value(x[i, b]) > 0:\n",
     "                    print(\n",
     "                        f\"Item {i} weight: {data['weights'][i]} value: {data['values'][i]}\"\n",
     "                    )\n",
-    "                    bin_weight += data['weights'][i]\n",
-    "                    bin_value += data['values'][i]\n",
-    "            print(f'Packed bin weight: {bin_weight}')\n",
-    "            print(f'Packed bin value: {bin_value}\\n')\n",
+    "                    bin_weight += data[\"weights\"][i]\n",
+    "                    bin_value += data[\"values\"][i]\n",
+    "            print(f\"Packed bin weight: {bin_weight}\")\n",
+    "            print(f\"Packed bin value: {bin_value}\\n\")\n",
     "            total_weight += bin_weight\n",
-    "        print(f'Total packed weight: {total_weight}')\n",
+    "        print(f\"Total packed weight: {total_weight}\")\n",
     "    else:\n",
-    "        print('The problem does not have an optimal solution.')\n",
+    "        print(\"The problem does not have an optimal solution.\")\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/sat/no_overlap_sample_sat.ipynb b/examples/notebook/sat/no_overlap_sample_sat.ipynb
index b880cdff15..6375a78a29 100644
--- a/examples/notebook/sat/no_overlap_sample_sat.ipynb
+++ b/examples/notebook/sat/no_overlap_sample_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Code sample to demonstrate how to build a NoOverlap constraint.\n"
    ]
   },
@@ -91,33 +92,32 @@
     "    horizon = 21  # 3 weeks.\n",
     "\n",
     "    # Task 0, duration 2.\n",
-    "    start_0 = model.NewIntVar(0, horizon, 'start_0')\n",
+    "    start_0 = model.NewIntVar(0, horizon, \"start_0\")\n",
     "    duration_0 = 2  # Python cp/sat code accepts integer variables or constants.\n",
-    "    end_0 = model.NewIntVar(0, horizon, 'end_0')\n",
-    "    task_0 = model.NewIntervalVar(start_0, duration_0, end_0, 'task_0')\n",
+    "    end_0 = model.NewIntVar(0, horizon, \"end_0\")\n",
+    "    task_0 = model.NewIntervalVar(start_0, duration_0, end_0, \"task_0\")\n",
     "    # Task 1, duration 4.\n",
-    "    start_1 = model.NewIntVar(0, horizon, 'start_1')\n",
+    "    start_1 = model.NewIntVar(0, horizon, \"start_1\")\n",
     "    duration_1 = 4  # Python cp/sat code accepts integer variables or constants.\n",
-    "    end_1 = model.NewIntVar(0, horizon, 'end_1')\n",
-    "    task_1 = model.NewIntervalVar(start_1, duration_1, end_1, 'task_1')\n",
+    "    end_1 = model.NewIntVar(0, horizon, \"end_1\")\n",
+    "    task_1 = model.NewIntervalVar(start_1, duration_1, end_1, \"task_1\")\n",
     "\n",
     "    # Task 2, duration 3.\n",
-    "    start_2 = model.NewIntVar(0, horizon, 'start_2')\n",
+    "    start_2 = model.NewIntVar(0, horizon, \"start_2\")\n",
     "    duration_2 = 3  # Python cp/sat code accepts integer variables or constants.\n",
-    "    end_2 = model.NewIntVar(0, horizon, 'end_2')\n",
-    "    task_2 = model.NewIntervalVar(start_2, duration_2, end_2, 'task_2')\n",
+    "    end_2 = model.NewIntVar(0, horizon, \"end_2\")\n",
+    "    task_2 = model.NewIntervalVar(start_2, duration_2, end_2, \"task_2\")\n",
     "\n",
     "    # Weekends.\n",
-    "    weekend_0 = model.NewIntervalVar(5, 2, 7, 'weekend_0')\n",
-    "    weekend_1 = model.NewIntervalVar(12, 2, 14, 'weekend_1')\n",
-    "    weekend_2 = model.NewIntervalVar(19, 2, 21, 'weekend_2')\n",
+    "    weekend_0 = model.NewIntervalVar(5, 2, 7, \"weekend_0\")\n",
+    "    weekend_1 = model.NewIntervalVar(12, 2, 14, \"weekend_1\")\n",
+    "    weekend_2 = model.NewIntervalVar(19, 2, 21, \"weekend_2\")\n",
     "\n",
     "    # No Overlap constraint.\n",
-    "    model.AddNoOverlap(\n",
-    "        [task_0, task_1, task_2, weekend_0, weekend_1, weekend_2])\n",
+    "    model.AddNoOverlap([task_0, task_1, task_2, weekend_0, weekend_1, weekend_2])\n",
     "\n",
     "    # Makespan objective.\n",
-    "    obj = model.NewIntVar(0, horizon, 'makespan')\n",
+    "    obj = model.NewIntVar(0, horizon, \"makespan\")\n",
     "    model.AddMaxEquality(obj, [end_0, end_1, end_2])\n",
     "    model.Minimize(obj)\n",
     "\n",
@@ -127,12 +127,12 @@
     "\n",
     "    if status == cp_model.OPTIMAL:\n",
     "        # Print out makespan and the start times for all tasks.\n",
-    "        print('Optimal Schedule Length: %i' % solver.ObjectiveValue())\n",
-    "        print('Task 0 starts at %i' % solver.Value(start_0))\n",
-    "        print('Task 1 starts at %i' % solver.Value(start_1))\n",
-    "        print('Task 2 starts at %i' % solver.Value(start_2))\n",
+    "        print(f\"Optimal Schedule Length: {solver.ObjectiveValue()}\")\n",
+    "        print(f\"Task 0 starts at {solver.Value(start_0)}\")\n",
+    "        print(f\"Task 1 starts at {solver.Value(start_1)}\")\n",
+    "        print(f\"Task 2 starts at {solver.Value(start_2)}\")\n",
     "    else:\n",
-    "        print('Solver exited with nonoptimal status: %i' % status)\n",
+    "        print(f\"Solver exited with nonoptimal status: {status}\")\n",
     "\n",
     "\n",
     "NoOverlapSampleSat()\n",
diff --git a/examples/notebook/sat/non_linear_sat.ipynb b/examples/notebook/sat/non_linear_sat.ipynb
index 68ed4f838f..56713a44f0 100644
--- a/examples/notebook/sat/non_linear_sat.ipynb
+++ b/examples/notebook/sat/non_linear_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Non linear example.\n",
     "\n",
     "Finds a rectangle with maximum available area for given perimeter using\n",
@@ -95,11 +96,11 @@
     "\n",
     "    model = cp_model.CpModel()\n",
     "\n",
-    "    x = model.NewIntVar(0, perimeter, 'x')\n",
-    "    y = model.NewIntVar(0, perimeter, 'y')\n",
+    "    x = model.NewIntVar(0, perimeter, \"x\")\n",
+    "    y = model.NewIntVar(0, perimeter, \"y\")\n",
     "    model.Add(2 * (x + y) == perimeter)\n",
     "\n",
-    "    area = model.NewIntVar(0, perimeter * perimeter, 's')\n",
+    "    area = model.NewIntVar(0, perimeter * perimeter, \"s\")\n",
     "    model.AddMultiplicationEquality(area, x, y)\n",
     "\n",
     "    model.Maximize(area)\n",
@@ -109,11 +110,11 @@
     "    status = solver.Solve(model)\n",
     "\n",
     "    if status == cp_model.OPTIMAL or status == cp_model.FEASIBLE:\n",
-    "        print('x = %i' % solver.Value(x))\n",
-    "        print('y = %i' % solver.Value(y))\n",
-    "        print('s = %i' % solver.Value(area))\n",
+    "        print(f\"x = {solver.Value(x)}\")\n",
+    "        print(f\"y = {solver.Value(y)}\")\n",
+    "        print(f\"s = {solver.Value(area)}\")\n",
     "    else:\n",
-    "        print('No solution found.')\n",
+    "        print(\"No solution found.\")\n",
     "\n",
     "\n",
     "non_linear_sat()\n",
diff --git a/examples/notebook/sat/nqueens_sat.ipynb b/examples/notebook/sat/nqueens_sat.ipynb
index 8b03e581bf..505203a1b9 100644
--- a/examples/notebook/sat/nqueens_sat.ipynb
+++ b/examples/notebook/sat/nqueens_sat.ipynb
@@ -102,8 +102,10 @@
     "\n",
     "    def on_solution_callback(self):\n",
     "        current_time = time.time()\n",
-    "        print('Solution %i, time = %f s' %\n",
-    "              (self.__solution_count, current_time - self.__start_time))\n",
+    "        print(\n",
+    "            f\"Solution {self.__solution_count}, \"\n",
+    "            f\"time = {current_time - self.__start_time} s\"\n",
+    "        )\n",
     "        self.__solution_count += 1\n",
     "\n",
     "        all_queens = range(len(self.__queens))\n",
@@ -111,9 +113,9 @@
     "            for j in all_queens:\n",
     "                if self.Value(self.__queens[j]) == i:\n",
     "                    # There is a queen in column j, row i.\n",
-    "                    print('Q', end=' ')\n",
+    "                    print(\"Q\", end=\" \")\n",
     "                else:\n",
-    "                    print('_', end=' ')\n",
+    "                    print(\"_\", end=\" \")\n",
     "            print()\n",
     "        print()\n",
     "\n",
@@ -126,9 +128,7 @@
     "    # Creates the variables.\n",
     "    # There are `board_size` number of variables, one for a queen in each column\n",
     "    # of the board. The value of each variable is the row that the queen is in.\n",
-    "    queens = [\n",
-    "        model.NewIntVar(0, board_size - 1, 'x%i' % i) for i in range(board_size)\n",
-    "    ]\n",
+    "    queens = [model.NewIntVar(0, board_size - 1, f\"x_{i}\") for i in range(board_size)]\n",
     "\n",
     "    # Creates the constraints.\n",
     "    # All rows must be different.\n",
@@ -145,11 +145,11 @@
     "    solver.Solve(model, solution_printer)\n",
     "\n",
     "    # Statistics.\n",
-    "    print('\\nStatistics')\n",
-    "    print(f'  conflicts      : {solver.NumConflicts()}')\n",
-    "    print(f'  branches       : {solver.NumBranches()}')\n",
-    "    print(f'  wall time      : {solver.WallTime()} s')\n",
-    "    print(f'  solutions found: {solution_printer.solution_count()}')\n",
+    "    print(\"\\nStatistics\")\n",
+    "    print(f\"  conflicts      : {solver.NumConflicts()}\")\n",
+    "    print(f\"  branches       : {solver.NumBranches()}\")\n",
+    "    print(f\"  wall time      : {solver.WallTime()} s\")\n",
+    "    print(f\"  solutions found: {solution_printer.solution_count()}\")\n",
     "\n",
     "\n",
     "# By default, solve the 8x8 problem.\n",
diff --git a/examples/notebook/sat/nurses_sat.ipynb b/examples/notebook/sat/nurses_sat.ipynb
index eb57060fdf..010643b90d 100644
--- a/examples/notebook/sat/nurses_sat.ipynb
+++ b/examples/notebook/sat/nurses_sat.ipynb
@@ -104,8 +104,7 @@
     "    for n in all_nurses:\n",
     "        for d in all_days:\n",
     "            for s in all_shifts:\n",
-    "                shifts[(n, d,\n",
-    "                        s)] = model.NewBoolVar('shift_n%id%is%i' % (n, d, s))\n",
+    "                shifts[(n, d, s)] = model.NewBoolVar(f\"shift_n{n}_d{d}_s{s}\")\n",
     "\n",
     "    # Each shift is assigned to exactly one nurse in the schedule period.\n",
     "    for d in all_days:\n",
@@ -140,7 +139,6 @@
     "    # Enumerate all solutions.\n",
     "    solver.parameters.enumerate_all_solutions = True\n",
     "\n",
-    "\n",
     "    class NursesPartialSolutionPrinter(cp_model.CpSolverSolutionCallback):\n",
     "        \"\"\"Print intermediate solutions.\"\"\"\n",
     "\n",
@@ -155,19 +153,19 @@
     "\n",
     "        def on_solution_callback(self):\n",
     "            self._solution_count += 1\n",
-    "            print('Solution %i' % self._solution_count)\n",
+    "            print(f\"Solution {self._solution_count}\")\n",
     "            for d in range(self._num_days):\n",
-    "                print('Day %i' % d)\n",
+    "                print(f\"Day {d}\")\n",
     "                for n in range(self._num_nurses):\n",
     "                    is_working = False\n",
     "                    for s in range(self._num_shifts):\n",
     "                        if self.Value(self._shifts[(n, d, s)]):\n",
     "                            is_working = True\n",
-    "                            print('  Nurse %i works shift %i' % (n, s))\n",
+    "                            print(f\"  Nurse {n} works shift {s}\")\n",
     "                    if not is_working:\n",
-    "                        print('  Nurse {} does not work'.format(n))\n",
+    "                        print(f\"  Nurse {n} does not work\")\n",
     "            if self._solution_count >= self._solution_limit:\n",
-    "                print('Stop search after %i solutions' % self._solution_limit)\n",
+    "                print(f\"Stop search after {self._solution_limit} solutions\")\n",
     "                self.StopSearch()\n",
     "\n",
     "        def solution_count(self):\n",
@@ -175,18 +173,18 @@
     "\n",
     "    # Display the first five solutions.\n",
     "    solution_limit = 5\n",
-    "    solution_printer = NursesPartialSolutionPrinter(shifts, num_nurses,\n",
-    "                                                    num_days, num_shifts,\n",
-    "                                                    solution_limit)\n",
+    "    solution_printer = NursesPartialSolutionPrinter(\n",
+    "        shifts, num_nurses, num_days, num_shifts, solution_limit\n",
+    "    )\n",
     "\n",
     "    solver.Solve(model, solution_printer)\n",
     "\n",
     "    # Statistics.\n",
-    "    print('\\nStatistics')\n",
-    "    print('  - conflicts      : %i' % solver.NumConflicts())\n",
-    "    print('  - branches       : %i' % solver.NumBranches())\n",
-    "    print('  - wall time      : %f s' % solver.WallTime())\n",
-    "    print('  - solutions found: %i' % solution_printer.solution_count())\n",
+    "    print(\"\\nStatistics\")\n",
+    "    print(f\"  - conflicts      : {solver.NumConflicts()}\")\n",
+    "    print(f\"  - branches       : {solver.NumBranches()}\")\n",
+    "    print(f\"  - wall time      : {solver.WallTime()} s\")\n",
+    "    print(f\"  - solutions found: {solution_printer.solution_count()}\")\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/sat/optional_interval_sample_sat.ipynb b/examples/notebook/sat/optional_interval_sample_sat.ipynb
index 877b0b823b..a7983f313d 100644
--- a/examples/notebook/sat/optional_interval_sample_sat.ipynb
+++ b/examples/notebook/sat/optional_interval_sample_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Code sample to demonstrates how to build an optional interval.\n"
    ]
   },
@@ -91,26 +92,28 @@
     "    horizon = 100\n",
     "\n",
     "    # An interval can be created from three affine expressions.\n",
-    "    start_var = model.NewIntVar(0, horizon, 'start')\n",
+    "    start_var = model.NewIntVar(0, horizon, \"start\")\n",
     "    duration = 10  # Python cp/sat code accept integer variables or constants.\n",
-    "    end_var = model.NewIntVar(0, horizon, 'end')\n",
-    "    presence_var = model.NewBoolVar('presence')\n",
-    "    interval_var = model.NewOptionalIntervalVar(start_var, duration,\n",
-    "                                                end_var + 2, presence_var,\n",
-    "                                                'interval')\n",
+    "    end_var = model.NewIntVar(0, horizon, \"end\")\n",
+    "    presence_var = model.NewBoolVar(\"presence\")\n",
+    "    interval_var = model.NewOptionalIntervalVar(\n",
+    "        start_var, duration, end_var + 2, presence_var, \"interval\"\n",
+    "    )\n",
     "\n",
-    "    print(f'interval = {repr(interval_var)}')\n",
+    "    print(f\"interval = {repr(interval_var)}\")\n",
     "\n",
     "    # If the size is fixed, a simpler version uses the start expression and the\n",
     "    # size.\n",
     "    fixed_size_interval_var = model.NewOptionalFixedSizeIntervalVar(\n",
-    "        start_var, 10, presence_var, 'fixed_size_interval_var')\n",
-    "    print(f'fixed_size_interval_var = {repr(fixed_size_interval_var)}')\n",
+    "        start_var, 10, presence_var, \"fixed_size_interval_var\"\n",
+    "    )\n",
+    "    print(f\"fixed_size_interval_var = {repr(fixed_size_interval_var)}\")\n",
     "\n",
     "    # A fixed interval can be created using the same API.\n",
     "    fixed_interval = model.NewOptionalFixedSizeIntervalVar(\n",
-    "        5, 10, presence_var, 'fixed_interval')\n",
-    "    print(f'fixed_interval = {repr(fixed_interval)}')\n",
+    "        5, 10, presence_var, \"fixed_interval\"\n",
+    "    )\n",
+    "    print(f\"fixed_interval = {repr(fixed_interval)}\")\n",
     "\n",
     "\n",
     "OptionalIntervalSampleSat()\n",
diff --git a/examples/notebook/sat/overlapping_intervals_sample_sat.ipynb b/examples/notebook/sat/overlapping_intervals_sample_sat.ipynb
index 81ffdf4a57..df7ed8f617 100644
--- a/examples/notebook/sat/overlapping_intervals_sample_sat.ipynb
+++ b/examples/notebook/sat/overlapping_intervals_sample_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Code sample to demonstrates how to detect if two intervals overlap.\n"
    ]
   },
@@ -96,7 +97,7 @@
     "    def on_solution_callback(self):\n",
     "        self.__solution_count += 1\n",
     "        for v in self.__variables:\n",
-    "            print('%s=%i' % (v, self.Value(v)), end=' ')\n",
+    "            print(f\"{v}={self.Value(v)}\", end=\" \")\n",
     "        print()\n",
     "\n",
     "    def solution_count(self):\n",
@@ -110,31 +111,33 @@
     "    horizon = 7\n",
     "\n",
     "    # First interval.\n",
-    "    start_var_a = model.NewIntVar(0, horizon, 'start_a')\n",
+    "    start_var_a = model.NewIntVar(0, horizon, \"start_a\")\n",
     "    duration_a = 3\n",
-    "    end_var_a = model.NewIntVar(0, horizon, 'end_a')\n",
-    "    unused_interval_var_a = model.NewIntervalVar(start_var_a, duration_a,\n",
-    "                                                 end_var_a, 'interval_a')\n",
+    "    end_var_a = model.NewIntVar(0, horizon, \"end_a\")\n",
+    "    unused_interval_var_a = model.NewIntervalVar(\n",
+    "        start_var_a, duration_a, end_var_a, \"interval_a\"\n",
+    "    )\n",
     "\n",
     "    # Second interval.\n",
-    "    start_var_b = model.NewIntVar(0, horizon, 'start_b')\n",
+    "    start_var_b = model.NewIntVar(0, horizon, \"start_b\")\n",
     "    duration_b = 2\n",
-    "    end_var_b = model.NewIntVar(0, horizon, 'end_b')\n",
-    "    unused_interval_var_b = model.NewIntervalVar(start_var_b, duration_b,\n",
-    "                                                 end_var_b, 'interval_b')\n",
+    "    end_var_b = model.NewIntVar(0, horizon, \"end_b\")\n",
+    "    unused_interval_var_b = model.NewIntervalVar(\n",
+    "        start_var_b, duration_b, end_var_b, \"interval_b\"\n",
+    "    )\n",
     "\n",
     "    # a_after_b Boolean variable.\n",
-    "    a_after_b = model.NewBoolVar('a_after_b')\n",
+    "    a_after_b = model.NewBoolVar(\"a_after_b\")\n",
     "    model.Add(start_var_a >= end_var_b).OnlyEnforceIf(a_after_b)\n",
     "    model.Add(start_var_a < end_var_b).OnlyEnforceIf(a_after_b.Not())\n",
     "\n",
     "    # b_after_a Boolean variable.\n",
-    "    b_after_a = model.NewBoolVar('b_after_a')\n",
+    "    b_after_a = model.NewBoolVar(\"b_after_a\")\n",
     "    model.Add(start_var_b >= end_var_a).OnlyEnforceIf(b_after_a)\n",
     "    model.Add(start_var_b < end_var_a).OnlyEnforceIf(b_after_a.Not())\n",
     "\n",
     "    # Result Boolean variable.\n",
-    "    a_overlaps_b = model.NewBoolVar('a_overlaps_b')\n",
+    "    a_overlaps_b = model.NewBoolVar(\"a_overlaps_b\")\n",
     "\n",
     "    # Option a: using only clauses\n",
     "    model.AddBoolOr(a_after_b, b_after_a, a_overlaps_b)\n",
@@ -145,8 +148,9 @@
     "    # model.AddExactlyOne(a_after_b, b_after_a, a_overlaps_b)\n",
     "\n",
     "    # Search for start values in increasing order for the two intervals.\n",
-    "    model.AddDecisionStrategy([start_var_a, start_var_b], cp_model.CHOOSE_FIRST,\n",
-    "                              cp_model.SELECT_MIN_VALUE)\n",
+    "    model.AddDecisionStrategy(\n",
+    "        [start_var_a, start_var_b], cp_model.CHOOSE_FIRST, cp_model.SELECT_MIN_VALUE\n",
+    "    )\n",
     "\n",
     "    # Create a solver and solve with a fixed search.\n",
     "    solver = cp_model.CpSolver()\n",
@@ -157,8 +161,7 @@
     "    solver.parameters.enumerate_all_solutions = True\n",
     "\n",
     "    # Search and print out all solutions.\n",
-    "    solution_printer = VarArraySolutionPrinter(\n",
-    "        [start_var_a, start_var_b, a_overlaps_b])\n",
+    "    solution_printer = VarArraySolutionPrinter([start_var_a, start_var_b, a_overlaps_b])\n",
     "    solver.Solve(model, solution_printer)\n",
     "\n",
     "\n",
diff --git a/examples/notebook/sat/rabbits_and_pheasants_sat.ipynb b/examples/notebook/sat/rabbits_and_pheasants_sat.ipynb
index 00b9c2b892..8ffebc82fb 100644
--- a/examples/notebook/sat/rabbits_and_pheasants_sat.ipynb
+++ b/examples/notebook/sat/rabbits_and_pheasants_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Rabbits and Pheasants quizz.\n"
    ]
   },
@@ -89,8 +90,8 @@
     "    \"\"\"Solves the rabbits + pheasants problem.\"\"\"\n",
     "    model = cp_model.CpModel()\n",
     "\n",
-    "    r = model.NewIntVar(0, 100, 'r')\n",
-    "    p = model.NewIntVar(0, 100, 'p')\n",
+    "    r = model.NewIntVar(0, 100, \"r\")\n",
+    "    p = model.NewIntVar(0, 100, \"p\")\n",
     "\n",
     "    # 20 heads.\n",
     "    model.Add(r + p == 20)\n",
@@ -102,8 +103,7 @@
     "    status = solver.Solve(model)\n",
     "\n",
     "    if status == cp_model.OPTIMAL:\n",
-    "        print('%i rabbits and %i pheasants' %\n",
-    "              (solver.Value(r), solver.Value(p)))\n",
+    "        print(f\"{solver.Value(r)} rabbits and {solver.Value(p)} pheasants\")\n",
     "\n",
     "\n",
     "RabbitsAndPheasantsSat()\n",
diff --git a/examples/notebook/sat/ranking_sample_sat.ipynb b/examples/notebook/sat/ranking_sample_sat.ipynb
index 21eee475ba..f079bcd0cd 100644
--- a/examples/notebook/sat/ranking_sample_sat.ipynb
+++ b/examples/notebook/sat/ranking_sample_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Code sample to demonstrates how to rank intervals.\n"
    ]
   },
@@ -88,16 +89,16 @@
     "def RankTasks(model, starts, presences, ranks):\n",
     "    \"\"\"This method adds constraints and variables to links tasks and ranks.\n",
     "\n",
-    "  This method assumes that all starts are disjoint, meaning that all tasks have\n",
-    "  a strictly positive duration, and they appear in the same NoOverlap\n",
-    "  constraint.\n",
+    "    This method assumes that all starts are disjoint, meaning that all tasks have\n",
+    "    a strictly positive duration, and they appear in the same NoOverlap\n",
+    "    constraint.\n",
     "\n",
-    "  Args:\n",
-    "    model: The CpModel to add the constraints to.\n",
-    "    starts: The array of starts variables of all tasks.\n",
-    "    presences: The array of presence variables of all tasks.\n",
-    "    ranks: The array of rank variables of all tasks.\n",
-    "  \"\"\"\n",
+    "    Args:\n",
+    "      model: The CpModel to add the constraints to.\n",
+    "      starts: The array of starts variables of all tasks.\n",
+    "      presences: The array of presence variables of all tasks.\n",
+    "      ranks: The array of rank variables of all tasks.\n",
+    "    \"\"\"\n",
     "\n",
     "    num_tasks = len(starts)\n",
     "    all_tasks = range(num_tasks)\n",
@@ -109,7 +110,7 @@
     "            if i == j:\n",
     "                precedences[(i, j)] = presences[i]\n",
     "            else:\n",
-    "                prec = model.NewBoolVar('%i before %i' % (i, j))\n",
+    "                prec = model.NewBoolVar(f\"{i} before {j}\")\n",
     "                precedences[(i, j)] = prec\n",
     "                model.Add(starts[i] < starts[j]).OnlyEnforceIf(prec)\n",
     "\n",
@@ -120,17 +121,13 @@
     "            if not cp_model.ObjectIsATrueLiteral(presences[i]):\n",
     "                tmp_array.append(presences[i].Not())\n",
     "                # Makes sure that if i is not performed, all precedences are false.\n",
-    "                model.AddImplication(presences[i].Not(),\n",
-    "                                     precedences[(i, j)].Not())\n",
-    "                model.AddImplication(presences[i].Not(),\n",
-    "                                     precedences[(j, i)].Not())\n",
+    "                model.AddImplication(presences[i].Not(), precedences[(i, j)].Not())\n",
+    "                model.AddImplication(presences[i].Not(), precedences[(j, i)].Not())\n",
     "            if not cp_model.ObjectIsATrueLiteral(presences[j]):\n",
     "                tmp_array.append(presences[j].Not())\n",
     "                # Makes sure that if j is not performed, all precedences are false.\n",
-    "                model.AddImplication(presences[j].Not(),\n",
-    "                                     precedences[(i, j)].Not())\n",
-    "                model.AddImplication(presences[j].Not(),\n",
-    "                                     precedences[(j, i)].Not())\n",
+    "                model.AddImplication(presences[j].Not(), precedences[(i, j)].Not())\n",
+    "                model.AddImplication(presences[j].Not(), precedences[(j, i)].Not())\n",
     "            # The following bool_or will enforce that for any two intervals:\n",
     "            #    i precedes j or j precedes i or at least one interval is not\n",
     "            #        performed.\n",
@@ -161,25 +158,24 @@
     "\n",
     "    # Creates intervals, half of them are optional.\n",
     "    for t in all_tasks:\n",
-    "        start = model.NewIntVar(0, horizon, 'start_%i' % t)\n",
+    "        start = model.NewIntVar(0, horizon, f\"start[{t}]\")\n",
     "        duration = t + 1\n",
-    "        end = model.NewIntVar(0, horizon, 'end_%i' % t)\n",
+    "        end = model.NewIntVar(0, horizon, f\"end[{t}]\")\n",
     "        if t < num_tasks // 2:\n",
-    "            interval = model.NewIntervalVar(start, duration, end,\n",
-    "                                            'interval_%i' % t)\n",
+    "            interval = model.NewIntervalVar(start, duration, end, f\"interval[{t}]\")\n",
     "            presence = True\n",
     "        else:\n",
-    "            presence = model.NewBoolVar('presence_%i' % t)\n",
-    "            interval = model.NewOptionalIntervalVar(start, duration, end,\n",
-    "                                                    presence,\n",
-    "                                                    'o_interval_%i' % t)\n",
+    "            presence = model.NewBoolVar(f\"presence[{t}]\")\n",
+    "            interval = model.NewOptionalIntervalVar(\n",
+    "                start, duration, end, presence, f\"o_interval[{t}]\"\n",
+    "            )\n",
     "        starts.append(start)\n",
     "        ends.append(end)\n",
     "        intervals.append(interval)\n",
     "        presences.append(presence)\n",
     "\n",
     "        # Ranks = -1 if and only if the tasks is not performed.\n",
-    "        ranks.append(model.NewIntVar(-1, num_tasks - 1, 'rank_%i' % t))\n",
+    "        ranks.append(model.NewIntVar(-1, num_tasks - 1, f\"rank[{t}]\"))\n",
     "\n",
     "    # Adds NoOverlap constraint.\n",
     "    model.AddNoOverlap(intervals)\n",
@@ -191,7 +187,7 @@
     "    model.Add(ranks[0] < ranks[1])\n",
     "\n",
     "    # Creates makespan variable.\n",
-    "    makespan = model.NewIntVar(0, horizon, 'makespan')\n",
+    "    makespan = model.NewIntVar(0, horizon, \"makespan\")\n",
     "    for t in all_tasks:\n",
     "        model.Add(ends[t] <= makespan).OnlyEnforceIf(presences[t])\n",
     "\n",
@@ -206,17 +202,21 @@
     "\n",
     "    if status == cp_model.OPTIMAL:\n",
     "        # Prints out the makespan and the start times and ranks of all tasks.\n",
-    "        print('Optimal cost: %i' % solver.ObjectiveValue())\n",
-    "        print('Makespan: %i' % solver.Value(makespan))\n",
+    "        print(f\"Optimal cost: {solver.ObjectiveValue()}\")\n",
+    "        print(f\"Makespan: {solver.Value(makespan)}\")\n",
     "        for t in all_tasks:\n",
     "            if solver.Value(presences[t]):\n",
-    "                print('Task %i starts at %i with rank %i' %\n",
-    "                      (t, solver.Value(starts[t]), solver.Value(ranks[t])))\n",
+    "                print(\n",
+    "                    f\"Task {t} starts at {solver.Value(starts[t])} \"\n",
+    "                    f\"with rank {solver.Value(ranks[t])}\"\n",
+    "                )\n",
     "            else:\n",
-    "                print('Task %i in not performed and ranked at %i' %\n",
-    "                      (t, solver.Value(ranks[t])))\n",
+    "                print(\n",
+    "                    f\"Task {t} in not performed \"\n",
+    "                    f\"and ranked at {solver.Value(ranks[t])}\"\n",
+    "                )\n",
     "    else:\n",
-    "        print('Solver exited with nonoptimal status: %i' % status)\n",
+    "        print(f\"Solver exited with nonoptimal status: {status}\")\n",
     "\n",
     "\n",
     "RankingSampleSat()\n",
diff --git a/examples/notebook/sat/reified_sample_sat.ipynb b/examples/notebook/sat/reified_sample_sat.ipynb
index a8e6d01e9e..4ae4603d11 100644
--- a/examples/notebook/sat/reified_sample_sat.ipynb
+++ b/examples/notebook/sat/reified_sample_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Simple model with a reified constraint.\n"
    ]
   },
@@ -89,9 +90,9 @@
     "    \"\"\"Showcase creating a reified constraint.\"\"\"\n",
     "    model = cp_model.CpModel()\n",
     "\n",
-    "    x = model.NewBoolVar('x')\n",
-    "    y = model.NewBoolVar('y')\n",
-    "    b = model.NewBoolVar('b')\n",
+    "    x = model.NewBoolVar(\"x\")\n",
+    "    y = model.NewBoolVar(\"y\")\n",
+    "    b = model.NewBoolVar(\"b\")\n",
     "\n",
     "    # First version using a half-reified bool and.\n",
     "    model.AddBoolAnd(x, y.Not()).OnlyEnforceIf(b)\n",
diff --git a/examples/notebook/sat/schedule_requests_sat.ipynb b/examples/notebook/sat/schedule_requests_sat.ipynb
index 80b5228cd4..0732f016e7 100644
--- a/examples/notebook/sat/schedule_requests_sat.ipynb
+++ b/examples/notebook/sat/schedule_requests_sat.ipynb
@@ -97,16 +97,13 @@
     "    all_nurses = range(num_nurses)\n",
     "    all_shifts = range(num_shifts)\n",
     "    all_days = range(num_days)\n",
-    "    shift_requests = [[[0, 0, 1], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 1],\n",
-    "                       [0, 1, 0], [0, 0, 1]],\n",
-    "                      [[0, 0, 0], [0, 0, 0], [0, 1, 0], [0, 1, 0], [1, 0, 0],\n",
-    "                       [0, 0, 0], [0, 0, 1]],\n",
-    "                      [[0, 1, 0], [0, 1, 0], [0, 0, 0], [1, 0, 0], [0, 0, 0],\n",
-    "                       [0, 1, 0], [0, 0, 0]],\n",
-    "                      [[0, 0, 1], [0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 0],\n",
-    "                       [1, 0, 0], [0, 0, 0]],\n",
-    "                      [[0, 0, 0], [0, 0, 1], [0, 1, 0], [0, 0, 0], [1, 0, 0],\n",
-    "                       [0, 1, 0], [0, 0, 0]]]\n",
+    "    shift_requests = [\n",
+    "        [[0, 0, 1], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 1], [0, 1, 0], [0, 0, 1]],\n",
+    "        [[0, 0, 0], [0, 0, 0], [0, 1, 0], [0, 1, 0], [1, 0, 0], [0, 0, 0], [0, 0, 1]],\n",
+    "        [[0, 1, 0], [0, 1, 0], [0, 0, 0], [1, 0, 0], [0, 0, 0], [0, 1, 0], [0, 0, 0]],\n",
+    "        [[0, 0, 1], [0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 0], [1, 0, 0], [0, 0, 0]],\n",
+    "        [[0, 0, 0], [0, 0, 1], [0, 1, 0], [0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 0]],\n",
+    "    ]\n",
     "\n",
     "    # Creates the model.\n",
     "    model = cp_model.CpModel()\n",
@@ -117,8 +114,7 @@
     "    for n in all_nurses:\n",
     "        for d in all_days:\n",
     "            for s in all_shifts:\n",
-    "                shifts[(n, d,\n",
-    "                        s)] = model.NewBoolVar('shift_n%id%is%i' % (n, d, s))\n",
+    "                shifts[(n, d, s)] = model.NewBoolVar(f\"shift_n{n}_d{d}_s{s}\")\n",
     "\n",
     "    # Each shift is assigned to exactly one nurse in .\n",
     "    for d in all_days:\n",
@@ -149,36 +145,42 @@
     "\n",
     "    # pylint: disable=g-complex-comprehension\n",
     "    model.Maximize(\n",
-    "        sum(shift_requests[n][d][s] * shifts[(n, d, s)] for n in all_nurses\n",
-    "            for d in all_days for s in all_shifts))\n",
+    "        sum(\n",
+    "            shift_requests[n][d][s] * shifts[(n, d, s)]\n",
+    "            for n in all_nurses\n",
+    "            for d in all_days\n",
+    "            for s in all_shifts\n",
+    "        )\n",
+    "    )\n",
     "\n",
     "    # Creates the solver and solve.\n",
     "    solver = cp_model.CpSolver()\n",
     "    status = solver.Solve(model)\n",
     "\n",
     "    if status == cp_model.OPTIMAL:\n",
-    "        print('Solution:')\n",
+    "        print(\"Solution:\")\n",
     "        for d in all_days:\n",
-    "            print('Day', d)\n",
+    "            print(\"Day\", d)\n",
     "            for n in all_nurses:\n",
     "                for s in all_shifts:\n",
     "                    if solver.Value(shifts[(n, d, s)]) == 1:\n",
     "                        if shift_requests[n][d][s] == 1:\n",
-    "                            print('Nurse', n, 'works shift', s, '(requested).')\n",
+    "                            print(\"Nurse\", n, \"works shift\", s, \"(requested).\")\n",
     "                        else:\n",
-    "                            print('Nurse', n, 'works shift', s,\n",
-    "                                  '(not requested).')\n",
+    "                            print(\"Nurse\", n, \"works shift\", s, \"(not requested).\")\n",
     "            print()\n",
-    "        print(f'Number of shift requests met = {solver.ObjectiveValue()}',\n",
-    "              f'(out of {num_nurses * min_shifts_per_nurse})')\n",
+    "        print(\n",
+    "            f\"Number of shift requests met = {solver.ObjectiveValue()}\",\n",
+    "            f\"(out of {num_nurses * min_shifts_per_nurse})\",\n",
+    "        )\n",
     "    else:\n",
-    "        print('No optimal solution found !')\n",
+    "        print(\"No optimal solution found !\")\n",
     "\n",
     "    # Statistics.\n",
-    "    print('\\nStatistics')\n",
-    "    print('  - conflicts: %i' % solver.NumConflicts())\n",
-    "    print('  - branches : %i' % solver.NumBranches())\n",
-    "    print('  - wall time: %f s' % solver.WallTime())\n",
+    "    print(\"\\nStatistics\")\n",
+    "    print(f\"  - conflicts: {solver.NumConflicts()}\")\n",
+    "    print(f\"  - branches : {solver.NumBranches()}\")\n",
+    "    print(f\"  - wall time: {solver.WallTime()}s\")\n",
     "\n",
     "\n",
     "main()\n",
diff --git a/examples/notebook/sat/scheduling_with_calendar_sample_sat.ipynb b/examples/notebook/sat/scheduling_with_calendar_sample_sat.ipynb
index a58da5eec7..4a4dc9aba5 100644
--- a/examples/notebook/sat/scheduling_with_calendar_sample_sat.ipynb
+++ b/examples/notebook/sat/scheduling_with_calendar_sample_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Code sample to demonstrate how an interval can span across a break.\n"
    ]
   },
@@ -96,7 +97,7 @@
     "    def on_solution_callback(self):\n",
     "        self.__solution_count += 1\n",
     "        for v in self.__variables:\n",
-    "            print('%s=%i' % (v, self.Value(v)), end=' ')\n",
+    "            print(f\"{v}={self.Value(v)}\", end=\" \")\n",
     "        print()\n",
     "\n",
     "    def solution_count(self):\n",
@@ -116,23 +117,24 @@
     "    # Because of the break, work cannot start at 13h.\n",
     "\n",
     "    start = model.NewIntVarFromDomain(\n",
-    "        cp_model.Domain.FromIntervals([(8, 12), (14, 15)]), 'start')\n",
-    "    duration = model.NewIntVar(3, 4, 'duration')\n",
-    "    end = model.NewIntVar(8, 18, 'end')\n",
-    "    unused_interval = model.NewIntervalVar(start, duration, end, 'interval')\n",
+    "        cp_model.Domain.FromIntervals([(8, 12), (14, 15)]), \"start\"\n",
+    "    )\n",
+    "    duration = model.NewIntVar(3, 4, \"duration\")\n",
+    "    end = model.NewIntVar(8, 18, \"end\")\n",
+    "    unused_interval = model.NewIntervalVar(start, duration, end, \"interval\")\n",
     "\n",
     "    # We have 2 states (spanning across lunch or not)\n",
-    "    across = model.NewBoolVar('across')\n",
+    "    across = model.NewBoolVar(\"across\")\n",
     "    non_spanning_hours = cp_model.Domain.FromValues([8, 9, 10, 14, 15])\n",
     "    model.AddLinearExpressionInDomain(start, non_spanning_hours).OnlyEnforceIf(\n",
-    "        across.Not())\n",
+    "        across.Not()\n",
+    "    )\n",
     "    model.AddLinearConstraint(start, 11, 12).OnlyEnforceIf(across)\n",
     "    model.Add(duration == 3).OnlyEnforceIf(across.Not())\n",
     "    model.Add(duration == 4).OnlyEnforceIf(across)\n",
     "\n",
     "    # Search for x values in increasing order.\n",
-    "    model.AddDecisionStrategy([start], cp_model.CHOOSE_FIRST,\n",
-    "                              cp_model.SELECT_MIN_VALUE)\n",
+    "    model.AddDecisionStrategy([start], cp_model.CHOOSE_FIRST, cp_model.SELECT_MIN_VALUE)\n",
     "\n",
     "    # Create a solver and solve with a fixed search.\n",
     "    solver = cp_model.CpSolver()\n",
diff --git a/examples/notebook/sat/search_for_all_solutions_sample_sat.ipynb b/examples/notebook/sat/search_for_all_solutions_sample_sat.ipynb
index 5444cc4ebf..8893f0488f 100644
--- a/examples/notebook/sat/search_for_all_solutions_sample_sat.ipynb
+++ b/examples/notebook/sat/search_for_all_solutions_sample_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Code sample that solves a model and displays all solutions.\n"
    ]
   },
@@ -96,7 +97,7 @@
     "    def on_solution_callback(self):\n",
     "        self.__solution_count += 1\n",
     "        for v in self.__variables:\n",
-    "            print('%s=%i' % (v, self.Value(v)), end=' ')\n",
+    "            print(f\"{v}={self.Value(v)}\", end=\" \")\n",
     "        print()\n",
     "\n",
     "    def solution_count(self):\n",
@@ -110,9 +111,9 @@
     "\n",
     "    # Creates the variables.\n",
     "    num_vals = 3\n",
-    "    x = model.NewIntVar(0, num_vals - 1, 'x')\n",
-    "    y = model.NewIntVar(0, num_vals - 1, 'y')\n",
-    "    z = model.NewIntVar(0, num_vals - 1, 'z')\n",
+    "    x = model.NewIntVar(0, num_vals - 1, \"x\")\n",
+    "    y = model.NewIntVar(0, num_vals - 1, \"y\")\n",
+    "    z = model.NewIntVar(0, num_vals - 1, \"z\")\n",
     "\n",
     "    # Create the constraints.\n",
     "    model.Add(x != y)\n",
@@ -125,8 +126,8 @@
     "    # Solve.\n",
     "    status = solver.Solve(model, solution_printer)\n",
     "\n",
-    "    print('Status = %s' % solver.StatusName(status))\n",
-    "    print('Number of solutions found: %i' % solution_printer.solution_count())\n",
+    "    print(f\"Status = {solver.StatusName(status)}\")\n",
+    "    print(f\"Number of solutions found: {solution_printer.solution_count()}\")\n",
     "\n",
     "\n",
     "SearchForAllSolutionsSampleSat()\n",
diff --git a/examples/notebook/sat/simple_sat_program.ipynb b/examples/notebook/sat/simple_sat_program.ipynb
index b8b56e7372..8d1f98e25c 100644
--- a/examples/notebook/sat/simple_sat_program.ipynb
+++ b/examples/notebook/sat/simple_sat_program.ipynb
@@ -93,9 +93,9 @@
     "\n",
     "    # Creates the variables.\n",
     "    num_vals = 3\n",
-    "    x = model.NewIntVar(0, num_vals - 1, 'x')\n",
-    "    y = model.NewIntVar(0, num_vals - 1, 'y')\n",
-    "    z = model.NewIntVar(0, num_vals - 1, 'z')\n",
+    "    x = model.NewIntVar(0, num_vals - 1, \"x\")\n",
+    "    y = model.NewIntVar(0, num_vals - 1, \"y\")\n",
+    "    z = model.NewIntVar(0, num_vals - 1, \"z\")\n",
     "\n",
     "    # Creates the constraints.\n",
     "    model.Add(x != y)\n",
@@ -105,11 +105,11 @@
     "    status = solver.Solve(model)\n",
     "\n",
     "    if status == cp_model.OPTIMAL or status == cp_model.FEASIBLE:\n",
-    "        print('x = %i' % solver.Value(x))\n",
-    "        print('y = %i' % solver.Value(y))\n",
-    "        print('z = %i' % solver.Value(z))\n",
+    "        print(f\"x = {solver.Value(x)}\")\n",
+    "        print(f\"y = {solver.Value(y)}\")\n",
+    "        print(f\"z = {solver.Value(z)}\")\n",
     "    else:\n",
-    "        print('No solution found.')\n",
+    "        print(\"No solution found.\")\n",
     "\n",
     "\n",
     "SimpleSatProgram()\n",
diff --git a/examples/notebook/sat/solution_hinting_sample_sat.ipynb b/examples/notebook/sat/solution_hinting_sample_sat.ipynb
index 61f2fdefbc..baa7fc218c 100644
--- a/examples/notebook/sat/solution_hinting_sample_sat.ipynb
+++ b/examples/notebook/sat/solution_hinting_sample_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Code sample that solves a model using solution hinting.\n"
    ]
   },
@@ -92,9 +93,9 @@
     "\n",
     "    # Creates the variables.\n",
     "    num_vals = 3\n",
-    "    x = model.NewIntVar(0, num_vals - 1, 'x')\n",
-    "    y = model.NewIntVar(0, num_vals - 1, 'y')\n",
-    "    z = model.NewIntVar(0, num_vals - 1, 'z')\n",
+    "    x = model.NewIntVar(0, num_vals - 1, \"x\")\n",
+    "    y = model.NewIntVar(0, num_vals - 1, \"y\")\n",
+    "    z = model.NewIntVar(0, num_vals - 1, \"z\")\n",
     "\n",
     "    # Creates the constraints.\n",
     "    model.Add(x != y)\n",
@@ -110,8 +111,8 @@
     "    solution_printer = cp_model.VarArrayAndObjectiveSolutionPrinter([x, y, z])\n",
     "    status = solver.Solve(model, solution_printer)\n",
     "\n",
-    "    print('Status = %s' % solver.StatusName(status))\n",
-    "    print('Number of solutions found: %i' % solution_printer.solution_count())\n",
+    "    print(f\"Status = {solver.StatusName(status)}\")\n",
+    "    print(f\"Number of solutions found: {solution_printer.solution_count()}\")\n",
     "\n",
     "\n",
     "SolutionHintingSampleSat()\n",
diff --git a/examples/notebook/sat/solve_and_print_intermediate_solutions_sample_sat.ipynb b/examples/notebook/sat/solve_and_print_intermediate_solutions_sample_sat.ipynb
index db8ca7bba9..9744ddf2a9 100644
--- a/examples/notebook/sat/solve_and_print_intermediate_solutions_sample_sat.ipynb
+++ b/examples/notebook/sat/solve_and_print_intermediate_solutions_sample_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Solves an optimization problem and displays all intermediate solutions.\n"
    ]
   },
@@ -95,10 +96,10 @@
     "        self.__solution_count = 0\n",
     "\n",
     "    def on_solution_callback(self):\n",
-    "        print('Solution %i' % self.__solution_count)\n",
-    "        print('  objective value = %i' % self.ObjectiveValue())\n",
+    "        print(f\"Solution {self.__solution_count}\")\n",
+    "        print(f\"  objective value = {self.ObjectiveValue()}\")\n",
     "        for v in self.__variables:\n",
-    "            print('  %s = %i' % (v, self.Value(v)), end=' ')\n",
+    "            print(f\"  {v}={self.Value(v)}\", end=\" \")\n",
     "        print()\n",
     "        self.__solution_count += 1\n",
     "\n",
@@ -113,9 +114,9 @@
     "\n",
     "    # Creates the variables.\n",
     "    num_vals = 3\n",
-    "    x = model.NewIntVar(0, num_vals - 1, 'x')\n",
-    "    y = model.NewIntVar(0, num_vals - 1, 'y')\n",
-    "    z = model.NewIntVar(0, num_vals - 1, 'z')\n",
+    "    x = model.NewIntVar(0, num_vals - 1, \"x\")\n",
+    "    y = model.NewIntVar(0, num_vals - 1, \"y\")\n",
+    "    z = model.NewIntVar(0, num_vals - 1, \"z\")\n",
     "\n",
     "    # Creates the constraints.\n",
     "    model.Add(x != y)\n",
@@ -127,8 +128,8 @@
     "    solution_printer = VarArrayAndObjectiveSolutionPrinter([x, y, z])\n",
     "    status = solver.Solve(model, solution_printer)\n",
     "\n",
-    "    print('Status = %s' % solver.StatusName(status))\n",
-    "    print('Number of solutions found: %i' % solution_printer.solution_count())\n",
+    "    print(f\"Status = {solver.StatusName(status)}\")\n",
+    "    print(f\"Number of solutions found: {solution_printer.solution_count()}\")\n",
     "\n",
     "\n",
     "SolveAndPrintIntermediateSolutionsSampleSat()\n",
diff --git a/examples/notebook/sat/solve_with_time_limit_sample_sat.ipynb b/examples/notebook/sat/solve_with_time_limit_sample_sat.ipynb
index e96f17cb51..0fce1d2a2c 100644
--- a/examples/notebook/sat/solve_with_time_limit_sample_sat.ipynb
+++ b/examples/notebook/sat/solve_with_time_limit_sample_sat.ipynb
@@ -92,9 +92,9 @@
     "    model = cp_model.CpModel()\n",
     "    # Creates the variables.\n",
     "    num_vals = 3\n",
-    "    x = model.NewIntVar(0, num_vals - 1, 'x')\n",
-    "    y = model.NewIntVar(0, num_vals - 1, 'y')\n",
-    "    z = model.NewIntVar(0, num_vals - 1, 'z')\n",
+    "    x = model.NewIntVar(0, num_vals - 1, \"x\")\n",
+    "    y = model.NewIntVar(0, num_vals - 1, \"y\")\n",
+    "    z = model.NewIntVar(0, num_vals - 1, \"z\")\n",
     "    # Adds an all-different constraint.\n",
     "    model.Add(x != y)\n",
     "\n",
@@ -107,9 +107,9 @@
     "    status = solver.Solve(model)\n",
     "\n",
     "    if status == cp_model.OPTIMAL:\n",
-    "        print('x = %i' % solver.Value(x))\n",
-    "        print('y = %i' % solver.Value(y))\n",
-    "        print('z = %i' % solver.Value(z))\n",
+    "        print(f\"x = {solver.Value(x)}\")\n",
+    "        print(f\"y = {solver.Value(y)}\")\n",
+    "        print(f\"z = {solver.Value(z)}\")\n",
     "\n",
     "\n",
     "SolveWithTimeLimitSampleSat()\n",
diff --git a/examples/notebook/sat/step_function_sample_sat.ipynb b/examples/notebook/sat/step_function_sample_sat.ipynb
index a8a0931974..45bd53dff0 100644
--- a/examples/notebook/sat/step_function_sample_sat.ipynb
+++ b/examples/notebook/sat/step_function_sample_sat.ipynb
@@ -72,6 +72,7 @@
    "id": "description",
    "metadata": {},
    "source": [
+    "\n",
     "Implements a step function.\n"
    ]
   },
@@ -96,7 +97,7 @@
     "    def on_solution_callback(self):\n",
     "        self.__solution_count += 1\n",
     "        for v in self.__variables:\n",
-    "            print('%s=%i' % (v, self.Value(v)), end=' ')\n",
+    "            print(f\"{v}={self.Value(v)}\", end=\" \")\n",
     "        print()\n",
     "\n",
     "    def solution_count(self):\n",
@@ -110,7 +111,7 @@
     "    model = cp_model.CpModel()\n",
     "\n",
     "    # Declare our primary variable.\n",
-    "    x = model.NewIntVar(0, 20, 'x')\n",
+    "    x = model.NewIntVar(0, 20, \"x\")\n",
     "\n",
     "    # Create the expression variable and implement the step function\n",
     "    # Note it is not defined for x == 2.\n",
@@ -121,23 +122,24 @@
     "    #      -- ---            0\n",
     "    # 0 ================ 20\n",
     "    #\n",
-    "    expr = model.NewIntVar(0, 3, 'expr')\n",
+    "    expr = model.NewIntVar(0, 3, \"expr\")\n",
     "\n",
     "    # expr == 0 on [5, 6] U [8, 10]\n",
-    "    b0 = model.NewBoolVar('b0')\n",
+    "    b0 = model.NewBoolVar(\"b0\")\n",
     "    model.AddLinearExpressionInDomain(\n",
-    "        x, cp_model.Domain.FromIntervals([(5, 6), (8, 10)])).OnlyEnforceIf(b0)\n",
+    "        x, cp_model.Domain.FromIntervals([(5, 6), (8, 10)])\n",
+    "    ).OnlyEnforceIf(b0)\n",
     "    model.Add(expr == 0).OnlyEnforceIf(b0)\n",
     "\n",
     "    # expr == 2 on [0, 1] U [3, 4] U [11, 20]\n",
-    "    b2 = model.NewBoolVar('b2')\n",
+    "    b2 = model.NewBoolVar(\"b2\")\n",
     "    model.AddLinearExpressionInDomain(\n",
-    "        x, cp_model.Domain.FromIntervals([(0, 1), (3, 4),\n",
-    "                                          (11, 20)])).OnlyEnforceIf(b2)\n",
+    "        x, cp_model.Domain.FromIntervals([(0, 1), (3, 4), (11, 20)])\n",
+    "    ).OnlyEnforceIf(b2)\n",
     "    model.Add(expr == 2).OnlyEnforceIf(b2)\n",
     "\n",
     "    # expr == 3 when x == 7\n",
-    "    b3 = model.NewBoolVar('b3')\n",
+    "    b3 = model.NewBoolVar(\"b3\")\n",
     "    model.Add(x == 7).OnlyEnforceIf(b3)\n",
     "    model.Add(expr == 3).OnlyEnforceIf(b3)\n",
     "\n",
@@ -145,8 +147,7 @@
     "    model.AddBoolOr(b0, b2, b3)\n",
     "\n",
     "    # Search for x values in increasing order.\n",
-    "    model.AddDecisionStrategy([x], cp_model.CHOOSE_FIRST,\n",
-    "                              cp_model.SELECT_MIN_VALUE)\n",
+    "    model.AddDecisionStrategy([x], cp_model.CHOOSE_FIRST, cp_model.SELECT_MIN_VALUE)\n",
     "\n",
     "    # Create a solver and solve with a fixed search.\n",
     "    solver = cp_model.CpSolver()\n",
diff --git a/examples/notebook/sat/stop_after_n_solutions_sample_sat.ipynb b/examples/notebook/sat/stop_after_n_solutions_sample_sat.ipynb
index 4ac2dec17f..2effb8e508 100644
--- a/examples/notebook/sat/stop_after_n_solutions_sample_sat.ipynb
+++ b/examples/notebook/sat/stop_after_n_solutions_sample_sat.ipynb
@@ -98,10 +98,10 @@
     "    def on_solution_callback(self):\n",
     "        self.__solution_count += 1\n",
     "        for v in self.__variables:\n",
-    "            print('%s=%i' % (v, self.Value(v)), end=' ')\n",
+    "            print(f\"{v}={self.Value(v)}\", end=\" \")\n",
     "        print()\n",
     "        if self.__solution_count >= self.__solution_limit:\n",
-    "            print('Stop search after %i solutions' % self.__solution_limit)\n",
+    "            print(f\"Stop search after {self.__solution_limit} solutions\")\n",
     "            self.StopSearch()\n",
     "\n",
     "    def solution_count(self):\n",
@@ -114,9 +114,9 @@
     "    model = cp_model.CpModel()\n",
     "    # Creates the variables.\n",
     "    num_vals = 3\n",
-    "    x = model.NewIntVar(0, num_vals - 1, 'x')\n",
-    "    y = model.NewIntVar(0, num_vals - 1, 'y')\n",
-    "    z = model.NewIntVar(0, num_vals - 1, 'z')\n",
+    "    x = model.NewIntVar(0, num_vals - 1, \"x\")\n",
+    "    y = model.NewIntVar(0, num_vals - 1, \"y\")\n",
+    "    z = model.NewIntVar(0, num_vals - 1, \"z\")\n",
     "\n",
     "    # Create a solver and solve.\n",
     "    solver = cp_model.CpSolver()\n",
@@ -125,8 +125,8 @@
     "    solver.parameters.enumerate_all_solutions = True\n",
     "    # Solve.\n",
     "    status = solver.Solve(model, solution_printer)\n",
-    "    print('Status = %s' % solver.StatusName(status))\n",
-    "    print('Number of solutions found: %i' % solution_printer.solution_count())\n",
+    "    print(f\"Status = {solver.StatusName(status)}\")\n",
+    "    print(f\"Number of solutions found: {solution_printer.solution_count()}\")\n",
     "    assert solution_printer.solution_count() == 5\n",
     "\n",
     "\n",