message: Clarify tf.image.per_image_standardization documetation
  Make it clear that it can operate on batches of image
diff: @@ -1471,14 +1471,16 @@ def resize_image_with_pad_v2(image,
  @tf_export('image.per_image_standardization')
  def per_image_standardization(image):
  - """Linearly scales `image` to have zero mean and unit variance.
  + """Linearly scales each image in `image` to have zero mean and unit variance.
  - This op computes `(x - mean) / adjusted_stddev`, where `mean` is the average
  - of all values in image, and
  - `adjusted_stddev = max(stddev, 1.0/sqrt(image.NumElements()))`.
  + For each image `x` in `image`, computes `(x - mean) / adjusted_stddev`, where
  - `stddev` is the standard deviation of all values in `image`. It is capped
  - away from zero to protect against division by 0 when handling uniform images.
  + - `mean` is the average of all values in `x`
  + - `adjusted_stddev = max(stddev, 1.0/sqrt(N))`
  + - `N` is the number of elements in `x`
  + - `stddev` is the standard deviation of all values in `x`
  + - `adjusted_stddev` is capped away from zero to protect against division by 0
  + when handling uniform images
  Args:
  image: An n-D Tensor where the last 3 dimensions are `[height, width,
  @@ -2923,7 +2925,7 @@ def _ssim_helper(x, y, reducer, max_val, compensation=1.0, k1=0.01, k2=0.03):
  x: First set of images.
  y: Second set of images.
  reducer: Function that computes 'local' averages from set of images. For
  - non-covolutional version, this is usually tf.reduce_mean(x, [1, 2]), and
  + non-convolutional version, this is usually tf.reduce_mean(x, [1, 2]), and
  for convolutional version, this is usually tf.nn.avg_pool2d or
  tf.nn.conv2d with weighted-sum kernel.
  max_val: The dynamic range (i.e., the difference between the maximum
  