added the ToC

examples/distances/distances.ipynb

@@ -15,6 +15,24 @@
     " functions available in [1] and show how they can be used with\n",
     " aeon and scikit-learn estimators on [this page](sklearn_distances.ipynb).\n",
     "\n",
+    " ---\n",
+    "\n",
+    "## **Navigation**\n",
+    "- [Introduction](#introduction)\n",
+    "- [Simple Distance Use Case](#simple-use-case-for-distances)\n",
+    "- [Basic Motivation for Elastic Distances](#Basic-Motivation-for-Elastic-Distances)\n",
+    "- [Dynamic Time Warping](#dynamic-time-warping)\n",
+    "- [Custom parameters for distances](#custom-parameters-for-distances)\n",
+    "- [Pairwise Distances](#pairwise-distances)\n",
+    "- [Alignment Paths](#alignment-paths)\n",
+    "- [Multivariate Distances](#multivariate-distances)\n",
+    "- [Unequal Length Series](#unequal-length-series)\n",
+    "- [Other Distance Functions](#other-distance-functions)\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## **Introduction**\n",
+    "\n",
     "The goal of a distance computation is to measure the dis-similarity between the time\n",
     "series `a` and `b`. A distance function should take `a` and `b` as parameters and return\n",
     "a float that is the computed distance between `a` and `b`. The value returned should\n",