Update CommunicationOptions link, revert BackupAndRestore to stable for v2.8

Author: 8bitmp3

site/en/tutorials/distribute/multi_worker_with_keras.ipynb

@@ -459,7 +459,7 @@
         "id": "N0iv7SyyAohc"
       },
       "source": [
-        "Note: `TF_CONFIG` is parsed and TensorFlow's GRPC servers are started at the time `MultiWorkerMirroredStrategy()` is called, so the `TF_CONFIG` environment variable must be set before a `tf.distribute.Strategy` instance is created. Since `TF_CONFIG` is not set yet, the above strategy is effectively single-worker training."
+        "Note: `TF_CONFIG` is parsed and TensorFlow's GRPC servers are started at the time `MultiWorkerMirroredStrategy` is called, so the `TF_CONFIG` environment variable must be set before a `tf.distribute.Strategy` instance is created. Since `TF_CONFIG` is not set yet, the above strategy is effectively single-worker training."
       ]
     },
     {
@@ -468,7 +468,7 @@
         "id": "FMy2VM4Akzpr"
       },
       "source": [
-        "`MultiWorkerMirroredStrategy` provides multiple implementations via the [`CommunicationOptions`](https://www.tensorflow.org/api_docs/python/tf/distribute/experimental/CommunicationOptions) parameter: 1) `RING` implements ring-based collectives using gRPC as the cross-host communication layer; 2) `NCCL` uses the [NVIDIA Collective Communication Library](https://developer.nvidia.com/nccl) to implement collectives; and 3) `AUTO` defers the choice to the runtime. The best choice of collective implementation depends upon the number and kind of GPUs, and the network interconnect in the cluster."
+        "`MultiWorkerMirroredStrategy` provides multiple implementations via the `tf.distribute.experimental.CommunicationOptions` parameter: 1) `RING` implements ring-based collectives using gRPC as the cross-host communication layer; 2) `NCCL` uses the [NVIDIA Collective Communication Library](https://developer.nvidia.com/nccl) to implement collectives; and 3) `AUTO` defers the choice to the runtime. The best choice of collective implementation depends upon the number and kind of GPUs, and the network interconnect in the cluster."
       ]
     },
     {
@@ -1145,7 +1145,7 @@
         "# Multi-worker training with `MultiWorkerMirroredStrategy`\n",
         "# and the `BackupAndRestore` callback.\n",
         "\n",
-        "callbacks = [tf.keras.callbacks.experimental.BackupAndRestore(backup_dir='/tmp/backup')]\n",
+        "callbacks = [tf.keras.callbacks.BackupAndRestore(backup_dir='/tmp/backup')]\n",
         "with strategy.scope():\n",
         "  multi_worker_model = mnist_setup.build_and_compile_cnn_model()\n",
         "multi_worker_model.fit(multi_worker_dataset,\n",