Modifications to the conclusion part in post-training quantization no…

…tebook in Pytorch

tutorials/notebooks/mct_features_notebooks/pytorch/example_pytorch_post_training_quantization.ipynb

@@ -380,7 +380,7 @@
     "\n",
     "The key advantage of hardware-friendly quantization is that the model can run more efficiently in terms of runtime, power consumption, and memory usage on designated hardware.\n",
     "\n",
-    "While this was a simple model and task, MCT can deliver competitive results across a wide range of tasks and network architectures. For more details, [check out the paper:](https://arxiv.org/abs/2109.09113).\n",
+    "MCT can deliver competitive results across a wide range of tasks and network architectures. For more details, [check out the paper:](https://arxiv.org/abs/2109.09113).\n",
     "\n",
     "## Copyrights\n",
     "\n",