formating

Author: manuelgloeckler

docs/advanced_tutorials/19_flowmatching_and_scorematching.ipynb

@@ -453,6 +453,7 @@
    ],
    "source": [
     "from sbi.utils.vector_field_utils import VectorFieldNet\n",
+    "\n",
     "?VectorFieldNet.forward"
    ]
   },
@@ -480,18 +481,18 @@
     "\n",
     "    def forward(self, theta, x, t):\n",
     "        # Requires 3 arguments for input (i.e. theta), condition (i.e. x) and \"time\" (a scalar)\n",
-    "        \n",
+    "\n",
     "        # Whatever weird things you want to do, you can do here.\n",
     "        h1 = self.in_layer1(x[...,None,:]).mean(-1)\n",
     "        h2 = self.in_layer2(theta[...,None,:]).mean(-1)\n",
     "        t = self.time_layer(t[...,None])\n",
     "        h = torch.relu(h1 + h2 + t)\n",
     "        out = self.out_layer(h)\n",
-    "        \n",
+    "\n",
     "        # Output dimension must exaclty match \"theta.shape\"\n",
     "        return out\n",
-    "    \n",
-    "net_base = CustomNetExample()    \n",
+    "\n",
+    "net_base = CustomNetExample()\n",
     "# This will still add some of `sbi` standard \"preconditioning\" for\n",
     "# diffusion/fmpe as well as automatic z-scoring transforms.\n",
     "# If you do want this you can also simple write your own builder.\n",