Add new tutorial sections to index

doc/source/differential-privacy-wrappers.rst

@@ -85,6 +85,7 @@ Performing the :math:`(\epsilon, \delta)` analysis
 Assume you have trained for :math:`n` rounds with sampling fraction :math:`q` and noise multiplier :math:`z`. In order to calculate the :math:`\epsilon` value this would result in for a particular :math:`\delta`, the following script may be used. 
 
 .. code-block:: python
+
    import tensorflow_privacy as tfp
    max_order = 32
    orders = range(2, max_order + 1)

doc/source/index.rst

@@ -34,6 +34,8 @@ A learning-oriented series of federated learning tutorials, the best place to st
 
    tutorial/Flower-1-Intro-to-FL-PyTorch
    tutorial/Flower-2-Strategies-in-FL-PyTorch
+   tutorial/Flower-3-Building-a-Strategy-PyTorch
+   tutorial/Flower-4-Client-and-NumPyClient-PyTorch
 
 .. toctree::
    :maxdepth: 1

doc/source/tutorial/Flower-1-Intro-to-FL-PyTorch.ipynb

@@ -664,7 +664,7 @@
         "\n",
         "There's a dedicated `#questions` channel if you need help, but we'd also love to hear who you are in `#introductions`!\n",
         "\n",
-        "[Part two](https://flower.dev/docs/tutorial/Flower-2-Strategies-in-FL-PyTorch.html) of the Flower tutorial goes into more depth about strategies and all the advanced things you can build with them."
+        "The [Flower Federated Learning Tutorial - Part 2](https://flower.dev/docs/tutorial/Flower-2-Strategies-in-FL-PyTorch.html) goes into more depth about strategies and all the advanced things you can build with them."
       ]
     }
   ],

doc/source/tutorial/Flower-2-Strategies-in-FL-PyTorch.ipynb

@@ -654,6 +654,19 @@
         "\n",
         "In the later sections, we've seen how we can communicate arbitrary values between server and clients to fully customize client-side execution. With that capability, we built a large-scale Federated Learning simulation using the Flower Virtual Client Engine and ran an experiment involving 1000 clients in the same workload - all in a Jupyter Notebook!"
       ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Next steps\n",
+        "\n",
+        "Before you continue, make sure to join the Flower community on Slack: [Join Slack](https://flower.dev/join-slack/)\n",
+        "\n",
+        "There's a dedicated `#questions` channel if you need help, but we'd also love to hear who you are in `#introductions`!\n",
+        "\n",
+        "The [Flower Federated Learning Tutorial - Part 3 [WIP]](https://flower.dev/docs/tutorial/Flower-3-Building-a-Strategy-PyTorch.html) shows how to build a fully custom `Strategy` from scratch."
+      ]
     }
   ],
   "metadata": {

doc/source/tutorial/Flower-3-Building-a-Strategy-PyTorch.ipynb

@@ -437,7 +437,11 @@
       "source": [
         "## Next steps\n",
         "\n",
-        "[WIP - add description]"
+        "Before you continue, make sure to join the Flower community on Slack: [Join Slack](https://flower.dev/join-slack/)\n",
+        "\n",
+        "There's a dedicated `#questions` channel if you need help, but we'd also love to hear who you are in `#introductions`!\n",
+        "\n",
+        "The [Flower Federated Learning Tutorial - Part 4](https://flower.dev/docs/tutorial/Flower-4-Client-and-NumPyClient-PyTorch.html) introduces `Client`, the flexible API underlying `NumPyClient`."
       ]
     }
   ],