PR comment correction (in progress)

tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_activation_threshold_search.ipynb

@@ -340,7 +340,7 @@
     "\n",
     "MCT’s `quantization_info` stores the threshold values for each layer. However, to view the actual activation distributions, the model needs to be reconstructed up to and including the target layer selected for visualization.\n",
     "\n",
-    "To do this, we first need to identify the layer names. In Keras, this can be easily done for the first 10 layers using the following code snippet."
+    "To do this, we first need to identify the layer names. "
    ]
   },
   {
@@ -353,8 +353,8 @@
    "outputs": [],
    "source": [
     "for index, (name, layer) in enumerate(float_model.named_modules()):\n",
-    "    if index < 10:\n",
-    "        print(name, layer)\n",
+    "    if index < 14:\n",
+    "        print(name)\n",
     "    else:\n",
     "        break"
    ]