removed obsolete statements from Horovod example

tensorflow/cifar10_distributed/train_model_horovod.py

@@ -67,20 +67,16 @@ def load_dataset(args):
     n_train, n_test = x_train.shape[0], x_test.shape[0]
     resize_size = 224 # use bigger images with ResNet
 
-    # disable any automatic data sharding in TensorFlow as we handle that manually here
-    # options = tf.data.Options()
-    # options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.OFF
-
     # Generating input pipelines
     ds_train = (tf.data.Dataset.from_tensor_slices((x_train, y_train))
                 .map(lambda image, label: (tf.image.resize(image, [resize_size, resize_size]), label))
                 .shuffle(n_train).shard(num_shards=hvd.size(), index=hvd.rank()) # Horovod: need to manually shard dataset
-                .cache().batch(args.batch_size).prefetch(tf.data.AUTOTUNE) #.with_options(options)
+                .cache().batch(args.batch_size).prefetch(tf.data.AUTOTUNE)
     )
     # Horovod: dont use sharding for test here. Otherwise reduction of results is necessary
     ds_test = (tf.data.Dataset.from_tensor_slices((x_test, y_test))
                .map(lambda image, label: (tf.image.resize(image, [resize_size, resize_size]), label))
-               .shuffle(n_test).cache().batch(args.batch_size).prefetch(tf.data.AUTOTUNE) #.with_options(options)
+               .shuffle(n_test).cache().batch(args.batch_size).prefetch(tf.data.AUTOTUNE)
     )
 
     # get updated shapes