revert

Author: jancervenka

site/en/guide/core/optimizers_core.ipynb

@@ -13,7 +13,6 @@
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "cellView": "form",
         "id": "AwOEIRJC6Une"
       },
       "outputs": [],
@@ -592,7 +591,10 @@
         "This notebook introduced the basics of writing and comparing optimizers with the [TensorFlow Core APIs](https://www.tensorflow.org/guide/core). Although prebuilt optimizers like Adam are generalizable, they may not always be the best choice for every model or dataset. Having fine-grained control over the optimization process can help streamline ML training workflows and improve overall performance. Refer to the following documentation for more examples of custom optimizers:\n",
         "\n",
         "*   This Adam optimizer is used in the [Multilayer perceptrons](https://www.tensorflow.org/guide/core/mlp_core) tutorial and the [Distributed training]()\n",
-        "*   [Model Garden](https://blog.tensorflow.org/2020/03/introducing-model-garden-for-tensorflow-2.html) has a variety of [custom optimizers](https://github.com/tensorflow/models/tree/master/official/modeling/optimization) written with the Core APIs.\n"
+        "*   [Model Garden](https://blog.tensorflow.org/2020/03/introducing-model-garden-for-tensorflow-2.html) has a variety of [custom optimizers](https://github.com/tensorflow/models/tree/master/official/modeling/optimization) written with the Core APIs.\n",
+        "\n",
+        "\n",
+        "\n"
       ]
     }
   ],