diff --git a/notebooks/brevitas-network-import.ipynb b/notebooks/brevitas-network-import.ipynb
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index 0000000000000000000000000000000000000000..cbe494e3165983ddc170989196c5c133e84b168b
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Importing Brevitas networks into FINN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import inspect\n",
+    "\n",
+    "def showSrc(what):\n",
+    "    print(\"\".join(inspect.getsourcelines(what)[0]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "class LFC(Module):\n",
+      "\n",
+      "    def __init__(self, num_classes=10, weight_bit_width=None, act_bit_width=None,\n",
+      "                 in_bit_width=None, in_ch=1, in_features=(28, 28)):\n",
+      "        super(LFC, self).__init__()\n",
+      "\n",
+      "        weight_quant_type = get_quant_type(weight_bit_width)\n",
+      "        act_quant_type = get_quant_type(act_bit_width)\n",
+      "        in_quant_type = get_quant_type(in_bit_width)\n",
+      "        stats_op = get_stats_op(weight_quant_type)\n",
+      "\n",
+      "        self.features = ModuleList()\n",
+      "        self.features.append(get_act_quant(in_bit_width, in_quant_type))\n",
+      "        self.features.append(Dropout(p=IN_DROPOUT))\n",
+      "        in_features = reduce(mul, in_features)\n",
+      "        for out_features in FC_OUT_FEATURES:\n",
+      "            self.features.append(get_quant_linear(in_features=in_features,\n",
+      "                                                  out_features=out_features,\n",
+      "                                                  per_out_ch_scaling=INTERMEDIATE_FC_PER_OUT_CH_SCALING,\n",
+      "                                                  bit_width=weight_bit_width,\n",
+      "                                                  quant_type=weight_quant_type,\n",
+      "                                                  stats_op=stats_op))\n",
+      "            in_features = out_features\n",
+      "            self.features.append(BatchNorm1d(num_features=in_features))\n",
+      "            self.features.append(get_act_quant(act_bit_width, act_quant_type))\n",
+      "            self.features.append(Dropout(p=HIDDEN_DROPOUT))\n",
+      "        self.fc = get_quant_linear(in_features=in_features,\n",
+      "                                   out_features=num_classes,\n",
+      "                                   per_out_ch_scaling=LAST_FC_PER_OUT_CH_SCALING,\n",
+      "                                   bit_width=weight_bit_width,\n",
+      "                                   quant_type=weight_quant_type,\n",
+      "                                   stats_op=stats_op)\n",
+      "\n",
+      "    def forward(self, x):\n",
+      "        x = x.view(x.shape[0], -1)\n",
+      "        x = 2.0 * x - torch.tensor([1.0])\n",
+      "        for mod in self.features:\n",
+      "            x = mod(x)\n",
+      "        out = self.fc(x)\n",
+      "        return out\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from models.LFC import LFC\n",
+    "showSrc(LFC)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: netron in /opt/conda/lib/python3.6/site-packages (3.5.9)\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install --user netron"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Serving 'LFCW1A1.onnx' at http://0.0.0.0:8081\n"
+     ]
+    }
+   ],
+   "source": [
+    "import netron\n",
+    "netron.start('LFCW1A1.onnx', port=8081, host=\"0.0.0.0\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<iframe src=\"http://0.0.0.0:8081/\" style=\"position: relative; width: 100%;\" height=\"400\"></iframe>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%html\n",
+    "<iframe src=\"http://0.0.0.0:8081/\" style=\"position: relative; width: 100%;\" height=\"400\"></iframe>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}