remove transform from tour nb

Author: RensDimmendaal

nbs/000_tour.ipynb

@@ -563,122 +563,6 @@
     "1 + L(2,3,4)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Transforms"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A `Transform` is the main building block of the fastai data pipelines. In the most general terms a transform can be any function you want to apply to your data, however the `Transform` class provides several mechanisms that make the process of building them easy and flexible (see the docs for information about each of these):\n",
-    "\n",
-    "- Type dispatch\n",
-    "- Dispatch over tuples\n",
-    "- Reversability\n",
-    "- Type propagation\n",
-    "- Preprocessing\n",
-    "- Filtering based on the dataset type\n",
-    "- Ordering\n",
-    "- Appending new behavior with decorators\n",
-    "\n",
-    "`Transform` looks for three special methods, <code>encodes</code>, <code>decodes</code>, and <code>setups</code>, which provide the implementation for [`__call__`](https://www.python-course.eu/python3_magic_methods.php), `decode`, and `setup` respectively. For instance:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "class A(Transform):\n",
-    "    def encodes(self, x): return x+1\n",
-    "\n",
-    "A()(1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For simple transforms like this, you can also use `Transform` as a decorator:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "@Transform\n",
-    "def f(x): return x+1\n",
-    "\n",
-    "f(1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Transforms can be composed into a `Pipeline`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.0"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "@Transform\n",
-    "def g(x): return x/2\n",
-    "\n",
-    "pipe = Pipeline([f,g])\n",
-    "pipe(3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The power of `Transform` and `Pipeline` is best understood by seeing how they're used to create a complete data processing pipeline. This is explained in [chapter 11](https://github.com/fastai/fastbook/blob/master/11_midlevel_data.ipynb) of the [fastai book](https://www.amazon.com/Deep-Learning-Coders-fastai-PyTorch/dp/1492045527), which is [available for free](https://github.com/fastai/fastbook) in Jupyter Notebook format."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,