message: Fix for 32890
  Usage fix for
diff: @@ -1394,7 +1394,7 @@ def batch_normalization(x,
  normalized over (the 'depth' dimension(s)), and dimension 1 for the
  others which are being normalized over.
  `mean` and `variance` in this case would typically be the outputs of
  - `tf.nn.moments(..., keep_dims=True)` during training, or running averages
  + `tf.nn.moments(..., keepdims=True)` during training, or running averages
  thereof during inference.
  * In the common case where the 'depth' dimension is the last dimension in
  the input tensor `x`, they may be one dimensional tensors of the same
  @@ -1403,10 +1403,10 @@ def batch_normalization(x,
  fully-connected layers, and `[batch, height, width, depth]` for
  convolutions.
  `mean` and `variance` in this case would typically be the outputs of
  - `tf.nn.moments(..., keep_dims=False)` during training, or running averages
  + `tf.nn.moments(..., keepdims=False)` during training, or running averages
  thereof during inference.
  - See Source: [Batch Normalization: Accelerating Deep Network Training by
  + See equation 11 in Algorithm 2 of source: [Batch Normalization: Accelerating Deep Network Training by
  Reducing Internal Covariate Shift; S. Ioffe, C. Szegedy]
  (http://arxiv.org/abs/1502.03167).
  @@ -1422,7 +1422,7 @@ def batch_normalization(x,
  name: A name for this operation (optional).
  Returns:
  - the normalized, scaled, offset tensor.
  + Normalized, scaled, offset tensor.
  """
  with ops.name_scope(name, "batchnorm", [x, mean, variance, scale, offset]):
  inv = math_ops.rsqrt(variance + variance_epsilon)
  