Refactor error messages and function signatures across multiple notebooks for clarity and consistency

- Updated error messages in Gauss method and numerical methods to use variables for better readability.
- Added return type hints to function signatures in various notebooks to improve code documentation.
- Corrected minor grammatical issues in docstrings for better clarity.
- Adjusted print statements and list concatenations for improved output formatting.
- Enhanced plotting functions to ensure consistent figure handling.

M2/Reinforcement Learning/Lab 2 - Maze Game as a Markov Decision Process Part 1.ipynb

@@ -381,7 +381,7 @@
     }
    ],
    "source": [
-    "def plot_maze_with_states():\n",
+    "def plot_maze_with_states() -> None:\n",
     "    \"\"\"Plot the maze with state indices.\"\"\"\n",
     "    grid = np.ones(\n",
     "        (n_rows, n_cols),\n",
@@ -391,7 +391,7 @@
     "            if maze_str[i][j] == \"#\":\n",
     "                grid[i, j] = 0  # We replace walls (#) with 0\n",
     "\n",
-    "    fig, ax = plt.subplots()\n",
+    "    _fig, ax = plt.subplots()\n",
     "    ax.imshow(grid, cmap=\"gray\", alpha=0.7)\n",
     "\n",
     "    # Plot state indices\n",
@@ -1142,7 +1142,7 @@
     "        ]  # For each reachable cell, we write the value V[s] in the grid.\n",
     "        # Walls # never get values, and they stay as NaN.\n",
     "\n",
-    "    fig, ax = plt.subplots()\n",
+    "    _fig, ax = plt.subplots()\n",
     "    im = ax.imshow(grid_values, cmap=\"magma\")\n",
     "    plt.colorbar(im, ax=ax)\n",
     "\n",