docs: regenerate the two notebooks with mangled code cells

notebooks/chapter_2_lens_modeling/tutorial_4_dealing_with_failure.ipynb

@@ -736,21 +736,24 @@
     "    unique_tag=dataset_name,\n",
     "    n_live=300,\n",
     "    iterations_per_quick_update=2500,  # Outpuers Notebook visualization of max likelihood model every N iterations\n",
-    ")\n",
-    "# %%\n",
-    "'''\n",
+    ")"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "__Run Time__\n",
     "\n",
     "The run time of the `log_likelihood_function` is around the usual value. \n",
     "\n",
     "Due to the more thorough Nautilus settings, the the model-fit should take more than 10000 iterations per free parameter \n",
     "to converge and thus take longer than we are used too.\n",
     "\n",
-    "Run the non-linear search.\n",
-    "'''"
-   ],
-   "outputs": [],
-   "execution_count": null
+    "Run the non-linear search."
+   ]
   },
   {
    "cell_type": "code",

notebooks/simulator/source_complex.ipynb

@@ -137,15 +137,18 @@
     "    centre_list=[(0.0, 0.0)],\n",
     ")\n",
     "\n",
-    "grid = grid.apply_over_sampling(over_sample_size=over_sample_size)\n",
-    "# %%\n",
-    "'''\n",
-    "Simulate a simple Gaussian PSF for the image.\n",
-    "'''"
+    "grid = grid.apply_over_sampling(over_sample_size=over_sample_size)"
    ],
    "outputs": [],
    "execution_count": null
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Simulate a simple Gaussian PSF for the image."
+   ]
+  },
   {
    "cell_type": "code",
    "metadata": {},