message: fix minor issues in tensorflow/tools/graph_transforms/README.md
diff: @@ -152,10 +152,10 @@ bazel-bin/tensorflow/tools/graph_transforms/transform_graph \
  The batch norm folding is included twice because there are two different flavors
  of batch normalization used in TensorFlow. The older version was implemented
  -with a single BatchNormWithGlobalNormalization op, but it was deprecated in
  -favor of a more recent approach using individual ops to implement the same
  -computation. The two transforms are in there so that both styles are recognized
  -and optimized.
  +with a single op like BatchNormWithGlobalNormalization or FusedBatchNorm, and
  +BatchNormWithGlobalNormalization was deprecated in favor of a more recent
  +approach using individual ops to implement the same computation. The two transforms
  +are in there so that both styles are recognized and optimized.
  ### Fixing Missing Kernel Errors on Mobile
  @@ -405,12 +405,12 @@ to continue on past transient errors, since this is just an optimization phase.
  Args: None \
  Prerequisites: None
  -In the early days of TensorFlow, batch normalization was implemented using a
  -single monolithic `BatchNormWithGlobalNormalization` op. In modern versions,
  -adding batch normalization from Python will give you a series of smaller math
  -ops instead, to achieve the same effect without special-purpose code. If you
  -have a graph that uses the older-style, this transform will recognize and
  -optimize those ops for inference, in the same way that the
  +In the early days of TensorFlow, batch normalization was implemented using
  +single monolithic ops like `BatchNormWithGlobalNormalization` or `FusedBatchNorm`.
  +In modern versions, adding batch normalization from Python will give you a series
  +of smaller math ops instead, to achieve the same effect without special-purpose
  +code. If you have a graph that uses the older-style, this transform will recognize
  +and optimize those ops for inference, in the same way that the
  [fold_batch_norms](#fold_batch_norms) transform does for the new approach.
  ### freeze_requantization_ranges
  