message: Do not force the mean and variance in training to be float32
  Summary: Create the variables with the same types as the input
  Test Plan: existing tests only
  Reviewers: jakeh, jamesn, georgep
diff: @@ -866,9 +866,9 @@ def fused_batch_norm(
  raise ValueError("Both 'mean' and 'variance' must be None "
  "if is_training is True.")
  if mean is None:
  - mean = constant_op.constant([])
  + mean = constant_op.constant(dtype=x.dtype, value=[])
  if variance is None:
  - variance = constant_op.constant([])
  + variance = constant_op.constant(dtype=x.dtype, value=[])
  # Set a minimum epsilon to 1.001e-5, which is a requirement by CUDNN to
  # prevent exception (see cudnn.h).
  min_epsilon = 1.001e-5
  