diff --git a/.gitignore b/.gitignore
index 8c717cf..8e76c9f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -7,3 +7,4 @@
 
 # tensorboard runs
 runs/
+.env

diff --git a/.gitignore b/.gitignore
index 8c717cf..8e76c9f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -7,3 +7,4 @@
 
 # tensorboard runs
 runs/
+.env
diff --git a/metapath2vec.ipynb b/metapath2vec.ipynb
index dac373f..6218f46 100644
--- a/metapath2vec.ipynb
+++ b/metapath2vec.ipynb
@@ -12,14 +12,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2021-11-03 03:47:25.594278: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
+      "2021-11-03 10:59:43.002841: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
       "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
      ]
     }
@@ -29,12 +29,14 @@
     "%autoreload 2\n",
     "\n",
     "import stellargraph as sg\n",
+    "from stellargraph.data import UniformRandomMetaPathWalk\n",
+    "\n",
     "import pandas as pd"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -88,31 +90,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Number of random walks: {len(walks)}\n"
+      "Number of random walks: 12\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "[['c1', 'c2', 'c1', 'c2', 'c1'],\n",
-       " ['c2', 'c1', 'c2', 'c1', 'c2'],\n",
+       "[['c1', 't1', 'c1', 't1', 'c1'],\n",
+       " ['c2'],\n",
        " ['c3'],\n",
        " ['t1', 'c1', 't1', 'c1', 't1'],\n",
        " ['t1', 'c1', 'c2'],\n",
+       " ['t1', 'c1', 'c2', 'c1', 't1'],\n",
        " ['t2'],\n",
        " ['t2'],\n",
+       " ['t2'],\n",
+       " ['t3'],\n",
        " ['t3'],\n",
        " ['t3']]"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -125,13 +130,11 @@
     "# metapath schemas as a list of lists of node types.\n",
     "metapaths = [\n",
     "    ['task', 'construct', 'task'],\n",
-    "    ['construct', 'construct'],\n",
+    "    ['construct', 'task', 'construct'],\n",
     "    ['task', 'construct', 'construct', 'task'],\n",
+    "    ['task', 'construct', 'construct', 'construct', 'task'],\n",
     "]\n",
     "\n",
-    "\n",
-    "from stellargraph.data import UniformRandomMetaPathWalk\n",
-    "\n",
     "rw = UniformRandomMetaPathWalk(G)\n",
     "\n",
     "walks = rw.run(\n",
@@ -141,7 +144,7 @@
     "    metapaths=metapaths,  # the metapaths\n",
     ")\n",
     "\n",
-    "print('Number of random walks: {len(walks)}')\n",
+    "print(f'Number of random walks: {len(walks)}')\n",
     "walks"
    ]
   },