diff --git a/lessons/python/yet_another_oop.ipynb b/lessons/python/yet_another_oop.ipynb
index 2193ed1..bbe9484 100644
--- a/lessons/python/yet_another_oop.ipynb
+++ b/lessons/python/yet_another_oop.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "16b65d76-7454-42ab-81cb-ada3dc221ba1",
    "metadata": {},
    "outputs": [],
@@ -57,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 372,
+   "execution_count": 2,
    "id": "4176e609-72bf-4670-b429-1a9d4476294a",
    "metadata": {},
    "outputs": [],
@@ -140,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 318,
+   "execution_count": 3,
    "id": "fa2dfe7d-1459-4f95-b74d-51a2f1404475",
    "metadata": {},
    "outputs": [],
@@ -163,7 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 319,
+   "execution_count": 4,
    "id": "e8160be4-5760-4136-bd19-ce6d9aa1b2e0",
    "metadata": {},
    "outputs": [
@@ -193,7 +193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 311,
+   "execution_count": 5,
    "id": "b005bf79-8e9a-422f-8155-35b6d74fc733",
    "metadata": {},
    "outputs": [
@@ -202,7 +202,7 @@
      "evalue": "incomplete input (546207509.py, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  Cell \u001b[0;32mIn[311], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    class MyModel:\u001b[0m\n\u001b[0m                  ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m incomplete input\n"
+      "\u001b[0;36m  Cell \u001b[0;32mIn[5], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    class MyModel:\u001b[0m\n\u001b[0m                  ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m incomplete input\n"
      ]
     }
    ],
@@ -222,7 +222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 326,
+   "execution_count": null,
    "id": "d0b74872-ef9e-42f0-a8a0-b3376012c137",
    "metadata": {},
    "outputs": [],
@@ -251,7 +251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 327,
+   "execution_count": null,
    "id": "0cb8522a-72f5-459e-940c-463b3f0f1c79",
    "metadata": {},
    "outputs": [],
@@ -275,21 +275,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 328,
+   "execution_count": null,
    "id": "e30f7525-7dc2-4970-bb56-d3ddd4918fb1",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "100"
-      ]
-     },
-     "execution_count": 328,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "model.a"
    ]
@@ -304,7 +293,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 332,
+   "execution_count": null,
    "id": "42d7ac94-966e-4c8b-b1d1-7651ccddff6d",
    "metadata": {},
    "outputs": [],
@@ -323,18 +312,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 334,
+   "execution_count": null,
    "id": "0220389b-771e-4e52-b0e8-980908b9cc35",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model = MyModel(2)\n",
     "model.add(2)\n",
@@ -343,18 +324,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 335,
+   "execution_count": null,
    "id": "ca1e24c7-fb41-48c6-9fe7-4a77a68f5af8",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(model.add_and_return(2))"
    ]
@@ -415,7 +388,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 379,
+   "execution_count": null,
    "id": "e5e42cab-583e-424e-ac5b-6ce0fe372d6b",
    "metadata": {},
    "outputs": [],
@@ -453,70 +426,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 363,
+   "execution_count": null,
    "id": "bc5b6258-ea43-4a08-b1a4-6c8c06ae9f87",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Help on class SimpleGlacier in module __main__:\n",
-      "\n",
-      "class SimpleGlacier(builtins.object)\n",
-      " |  SimpleGlacier(z: numpy.ndarray, ice_density: float = 917.0)\n",
-      " |  \n",
-      " |  Models the temperature profile with a glacier.\n",
-      " |  \n",
-      " |  This model is based off of: \n",
-      " |  The Physics of Glaciers (Cuffey and Paterson, 2010).\n",
-      " |  Lecture notes from Andy Aschwanden (McCarthy school, summer 2012).\n",
-      " |  \n",
-      " |  Attributes:\n",
-      " |      z: an array of z-coordinates\n",
-      " |  \n",
-      " |  Methods defined here:\n",
-      " |  \n",
-      " |  __init__(self, z: numpy.ndarray, ice_density: float = 917.0)\n",
-      " |      Initialize the model with an array of z-coordinates.\n",
-      " |  \n",
-      " |  run_one_step(self, dt: float) -> numpy.ndarray\n",
-      " |      Advance the model by one step of size dt.\n",
-      " |  \n",
-      " |  ----------------------------------------------------------------------\n",
-      " |  Data descriptors defined here:\n",
-      " |  \n",
-      " |  __dict__\n",
-      " |      dictionary for instance variables\n",
-      " |  \n",
-      " |  __weakref__\n",
-      " |      list of weak references to the object\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "help(SimpleGlacier)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 364,
+   "execution_count": null,
    "id": "56c97ced-a05d-4423-955f-c12e85e24b2d",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Help on function run_one_step in module __main__:\n",
-      "\n",
-      "run_one_step(self, dt: float) -> numpy.ndarray\n",
-      "    Advance the model by one step of size dt.\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "help(SimpleGlacier.run_one_step)"
    ]
@@ -531,18 +454,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 369,
+   "execution_count": null,
    "id": "3e62dcb7-d12f-4f5f-a720-46e17e4e17ca",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10000000000\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model = SimpleGlacier(np.arange(10), 10000000000)\n",
     "print(model.ice_density)"
@@ -558,18 +473,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 370,
+   "execution_count": null,
    "id": "4639ce75-4725-4db7-8e86-116a74e80a51",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "917.0\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model = SimpleGlacier(np.arange(10))\n",
     "print(model.ice_density)"
@@ -618,12 +525,14 @@
    "id": "4d4b08d0-7027-4fae-909e-4fe8d43c2113",
    "metadata": {},
    "source": [
-    "Just kidding - here's a template, but feel free to draw your own owl :)"
+    "Just kidding - here's a template, but feel free to draw your own owl :)\n",
+    "\n",
+    "Note: where I've written <tt>pass</tt>, you will probably want to <tt>return</tt> something or save a new class attribute (<tt>self.whatever</tt>), which means you can remove the <tt>pass</tt> statement."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": null,
    "id": "0fe24f77-be64-4235-88b6-b7f766d72be7",
    "metadata": {},
    "outputs": [],
@@ -646,9 +555,9 @@
     "        self.temperature = initial_temperature\n",
     "\n",
     "        # We probably want to calculate dz --> try using np.gradient\n",
-    "        self.dz = np.gradient(self.z)\n",
-    "        # Note: I didn't realize this when I originally wrote the class, but we won't need dz\n",
+    "        # Note: I didn't realize this when I originally wrote the class, but we won't actually need dz\n",
     "        # Want to know why? Check out the documentation for np.gradient -> https://numpy.org/doc/stable/reference/generated/numpy.gradient.html\n",
+    "        # (What does the second argument to np.gradient do?)\n",
     "        \n",
     "        # We'll need attributes for rho, c, and k\n",
     "        # You could also store info about boundary conditions at this point, if you want\n",
@@ -657,54 +566,37 @@
     "        self.k = 2.1\n",
     "\n",
     "        # Let's store boundary conditions too\n",
-    "        self.surface_temperature = -10.0\n",
-    "        self.basal_heat_flux = 0.0\n",
     "    \n",
     "        # Maybe we should go ahead and calculate diffusivity right away?\n",
-    "        self.kappa = self.calc_diffusivity()\n",
     "    \n",
     "        # Let's keep track of the elapsed time\n",
-    "        self.time_elapsed = 0.0\n",
     "\n",
     "    def calc_diffusivity(self) -> float:\n",
     "        \"\"\"From above, kappa = k / (rho * c).\"\"\"\n",
-    "        return self.k / (self.rho * self.c)\n",
+    "        pass\n",
     "\n",
     "    def calc_heat_flux(self) -> np.ndarray:\n",
     "        \"\"\"The heat flux is -kappa * dT / dz.\"\"\"\n",
     "        \n",
     "        # How should we calculate the difference in temperature with depth? (hint: see dz, above)\n",
-    "        temperature_gradient = np.gradient(self.temperature, self.z)\n",
     "\n",
     "        # Are dT and dz the same size? Are they the same size as z?\n",
-    "        assert temperature_gradient.shape == self.dz.shape == self.z.shape\n",
     "\n",
     "        # Don't forget to apply boundary conditions! The heat flux at the bed should be zero, for now.\n",
-    "        temperature_gradient[-1] = self.basal_heat_flux\n",
     "\n",
-    "        return -self.kappa * temperature_gradient\n",
+    "        pass\n",
     "        \n",
     "    def calc_divergence(self) -> np.ndarray:\n",
     "        \"\"\"In 1D, divergence is just the derivative. yay!\"\"\"\n",
-    "        heat_flux = self.calc_heat_flux()\n",
-    "        \n",
-    "        flux_divergence = np.gradient(heat_flux, self.z)\n",
-    "        \n",
-    "        return flux_divergence\n",
+    "        pass                \n",
     "    \n",
     "    def run_one_step(self, dt: float):\n",
     "        \"\"\"Advance the model by one step of size dt.\"\"\"\n",
-    "        flux_divergence = self.calc_divergence()\n",
-    "\n",
+    "        \n",
     "        # updated temperature = current temperature - the divergence * dt\n",
-    "        updated_temperature = self.temperature - flux_divergence * dt\n",
     "\n",
     "        # Don't forget to apply boundary conditions! The temperature at the surface is equal to a fixed value.\n",
-    "        updated_temperature[0] = self.surface_temperature\n",
-    "\n",
-    "        self.temperature = updated_temperature\n",
-    "\n",
-    "        self.time_elapsed += dt"
+    "        "
    ]
   },
   {
@@ -717,21 +609,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": null,
    "id": "d74164bf-7cbe-47cf-a825-9bc249684fff",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# The Greenland ice sheet is 3,053 m thick at the summit!\n",
     "z = np.arange(3053)\n",
@@ -761,57 +642,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": null,
    "id": "d216e3d2-986e-4d3f-82c1-e6ed2fbfbc43",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.05475818525543331 years.\n",
-      "54.81294344068874 years.\n",
-      "109.57112869612205 years.\n",
-      "164.32931395155535 years.\n",
-      "219.08749920698867 years.\n",
-      "273.84568446242196 years.\n",
-      "328.6038697178553 years.\n",
-      "383.3620549732886 years.\n",
-      "438.1202402287219 years.\n",
-      "492.87842548415523 years.\n",
-      "547.6366107395885 years.\n",
-      "602.3947959950218 years.\n",
-      "657.1529812504551 years.\n",
-      "711.9111665058884 years.\n",
-      "766.6693517613218 years.\n",
-      "821.4275370167551 years.\n",
-      "876.1857222721884 years.\n",
-      "930.9439075276217 years.\n",
-      "985.7020927830549 years.\n",
-      "1040.4602780384882 years.\n",
-      "1095.2184632939216 years.\n",
-      "1149.9766485493549 years.\n",
-      "1204.7348338047882 years.\n",
-      "1259.4930190602215 years.\n",
-      "1314.2512043156548 years.\n",
-      "1369.0093895710881 years.\n",
-      "1423.7675748265215 years.\n",
-      "1478.5257600819548 years.\n",
-      "1533.283945337388 years.\n",
-      "1588.0421305928214 years.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "model = SimpleGlacier(z, T0)\n",
     "\n",
@@ -840,61 +674,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
-   "id": "42bbf2f2-3791-4e8d-b4dc-066d3d50a5d1",
+   "execution_count": null,
+   "id": "dccba120-feb5-4ae6-b726-99146df0700f",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.05475818525543331 years.\n",
-      "54.81294344068874 years.\n",
-      "109.57112869612205 years.\n",
-      "164.32931395155535 years.\n",
-      "219.08749920698867 years.\n",
-      "273.84568446242196 years.\n",
-      "328.6038697178553 years.\n",
-      "383.3620549732886 years.\n",
-      "438.1202402287219 years.\n",
-      "492.87842548415523 years.\n",
-      "547.6366107395885 years.\n",
-      "602.3947959950218 years.\n",
-      "657.1529812504551 years.\n",
-      "711.9111665058884 years.\n",
-      "766.6693517613218 years.\n",
-      "821.4275370167551 years.\n",
-      "876.1857222721884 years.\n",
-      "930.9439075276217 years.\n",
-      "985.7020927830549 years.\n",
-      "1040.4602780384882 years.\n",
-      "1095.2184632939216 years.\n",
-      "1149.9766485493549 years.\n",
-      "1204.7348338047882 years.\n",
-      "1259.4930190602215 years.\n",
-      "1314.2512043156548 years.\n",
-      "1369.0093895710881 years.\n",
-      "1423.7675748265215 years.\n",
-      "1478.5257600819548 years.\n",
-      "1533.283945337388 years.\n",
-      "1588.0421305928214 years.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
+   "source": [
+    "# Initialize your model with a new bottom boundary condition\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4cc607e6-10bb-4dd5-9994-b3076be2a093",
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "model = SimpleGlacier(z, T0)\n",
-    "model.basal_heat_flux = 0.01\n",
-    "\n",
     "dt = 60 * 60 * 24 * 20\n",
     "\n",
     "for i in range(30000): \n",
@@ -910,6 +705,112 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "038ac06d-9307-4717-987a-5dece0a6b682",
+   "metadata": {},
+   "source": [
+    "### Spoilers below!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "038be991-e91b-488f-b423-f9ad2d833d8b",
+   "metadata": {},
+   "source": [
+    "This is one example of how you could choose to write this class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a74ed31d-6be6-40cb-a018-89830b53b923",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class SimpleGlacier:\n",
+    "    \"\"\"Models the temperature profile with a glacier.\n",
+    "    \n",
+    "    This model is based off of: \n",
+    "    The Physics of Glaciers (Cuffey and Paterson, 2010).\n",
+    "    Lecture notes from Andy Aschwanden (McCarthy school, summer 2012).\n",
+    "\n",
+    "    Attributes:\n",
+    "        z: an array of z-coordinates\n",
+    "        temperature: an array representing the temperature profile with depth\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, z: np.ndarray, initial_temperature: np.ndarray):\n",
+    "        \"\"\"Initialize the model with arrays of z-coordinates and initial temperature profile.\"\"\"\n",
+    "        self.z = z\n",
+    "        self.temperature = initial_temperature\n",
+    "\n",
+    "        # We probably want to calculate dz --> try using np.gradient\n",
+    "        self.dz = np.gradient(self.z)\n",
+    "        # Note: I didn't realize this when I originally wrote the class, but we won't need dz\n",
+    "        # Want to know why? Check out the documentation for np.gradient -> https://numpy.org/doc/stable/reference/generated/numpy.gradient.html\n",
+    "        \n",
+    "        # We'll need attributes for rho, c, and k\n",
+    "        # You could also store info about boundary conditions at this point, if you want\n",
+    "        self.rho = 917\n",
+    "        self.c = 2093\n",
+    "        self.k = 2.1\n",
+    "\n",
+    "        # Let's store boundary conditions too\n",
+    "        self.surface_temperature = -10.0\n",
+    "        self.basal_heat_flux = 0.0\n",
+    "    \n",
+    "        # Maybe we should go ahead and calculate diffusivity right away?\n",
+    "        self.kappa = self.calc_diffusivity()\n",
+    "    \n",
+    "        # Let's keep track of the elapsed time\n",
+    "        self.time_elapsed = 0.0\n",
+    "\n",
+    "    def calc_diffusivity(self) -> float:\n",
+    "        \"\"\"From above, kappa = k / (rho * c).\"\"\"\n",
+    "        return self.k / (self.rho * self.c)\n",
+    "\n",
+    "    def calc_heat_flux(self) -> np.ndarray:\n",
+    "        \"\"\"The heat flux is -kappa * dT / dz.\"\"\"\n",
+    "        \n",
+    "        # How should we calculate the difference in temperature with depth? (hint: see dz, above)\n",
+    "        temperature_gradient = np.gradient(self.temperature, self.z)\n",
+    "\n",
+    "        # Are dT and dz the same size? Are they the same size as z?\n",
+    "        assert temperature_gradient.shape == self.dz.shape == self.z.shape\n",
+    "\n",
+    "        # Don't forget to apply boundary conditions! The heat flux at the bed should be zero, for now.\n",
+    "        temperature_gradient[-1] = self.basal_heat_flux\n",
+    "\n",
+    "        return -self.kappa * temperature_gradient\n",
+    "        \n",
+    "    def calc_divergence(self) -> np.ndarray:\n",
+    "        \"\"\"In 1D, divergence is just the derivative. yay!\"\"\"\n",
+    "        heat_flux = self.calc_heat_flux()\n",
+    "        \n",
+    "        flux_divergence = np.gradient(heat_flux, self.z)\n",
+    "        \n",
+    "        return flux_divergence\n",
+    "    \n",
+    "    def run_one_step(self, dt: float):\n",
+    "        \"\"\"Advance the model by one step of size dt.\"\"\"\n",
+    "        flux_divergence = self.calc_divergence()\n",
+    "\n",
+    "        # updated temperature = current temperature - the divergence * dt\n",
+    "        updated_temperature = self.temperature - flux_divergence * dt\n",
+    "\n",
+    "        # Don't forget to apply boundary conditions! The temperature at the surface is equal to a fixed value.\n",
+    "        updated_temperature[0] = self.surface_temperature\n",
+    "\n",
+    "        self.temperature = updated_temperature\n",
+    "\n",
+    "        self.time_elapsed += dt"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "2d0e9161-1824-4be5-8f17-a3ea322e01f6",
@@ -938,21 +839,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 542,
+   "execution_count": null,
    "id": "7de68364-01b1-4c34-8c43-4392411f5a4a",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Replace test_array with your results\n",
     "test_array = np.random.random((10, 10))\n",
@@ -994,7 +884,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 373,
+   "execution_count": null,
    "id": "d54d9620-8be3-44d5-846f-cf58b616030b",
    "metadata": {},
    "outputs": [],
@@ -1019,7 +909,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 548,
+   "execution_count": null,
    "id": "9bdc456b-4425-48ef-8b5f-aa9a42a33abe",
    "metadata": {},
    "outputs": [],
@@ -1036,21 +926,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 547,
+   "execution_count": null,
    "id": "df9ab5b5-a885-4219-b49f-32cbf7dfee25",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Help on function run_one_step in module __main__:\n",
-      "\n",
-      "run_one_step(self)\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "help(DiffusionChild.run_one_step)"
    ]
@@ -1065,22 +944,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 549,
+   "execution_count": null,
    "id": "1474651c-a28b-4c5a-92ed-271be7b98b9a",
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "type object 'DiffusionModel1D' has no attribute 'no_parents_allowed'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[549], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m help(\u001b[43mDiffusionModel1D\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mno_parents_allowed\u001b[49m)\n",
-      "\u001b[0;31mAttributeError\u001b[0m: type object 'DiffusionModel1D' has no attribute 'no_parents_allowed'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "help(DiffusionModel1D.no_parents_allowed)"
    ]