Added docs to comprehensive ipynb example.

Change-Id: I768184253ec5bae3e240219e5d584fee361b55d8

Author: wwwind

tensorflow_model_optimization/g3doc/guide/clustering/clustering_comprehensive_guide.ipynb

@@ -279,6 +279,25 @@
         "clustered_model.summary()"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Cluster convolutional layers per channel\n",
+        "\n",
+        "The clustered model could be passed to further optimizations such as a [post training quantization](https://www.tensorflow.org/lite/performance/post_training_quantization). If the quantization is done per channel, then the model should be clustered per channel as well. This increases the accuracy of the clustered and quantized model\n",
+        "\n",
+        "**Note:** only Conv2D layers are clustered per channel\n",
+        "\n",
+        "To cluster per channel, the parameter `cluster_per_channel` should be set to `True`. It could be set for some layers or for the whole model.\n",
+        "\n",
+        "**Tips:**\n",
+        "\n",
+        "* If a model is to be quantized further, you can consider to use [cluster preserving QAT technique](https://www.tensorflow.org/model_optimization/guide/combine/collaborative_optimization).\n",
+        "\n",
+        "* The model could be pruned before applying the clustering per channel. With the parameter `preserve_sparsity` is set to `True`, the sparsity is preserved during the clustering per channel. Note that the [sparsity and cluster preserving QAT technique](https://www.tensorflow.org/model_optimization/guide/combine/collaborative_optimization) should be used in this case."
+      ],
+      "metadata": {}
+    },
     {
       "cell_type": "markdown",
       "metadata": {