message: Consistent capitalization of Python in array_ops.py
diff: @@ -50,7 +50,7 @@ from tensorflow.python.util.tf_export import tf_export
  newaxis = None
  tf_export("newaxis").export_constant(__name__, "newaxis")
  -# We override the 'slice' for the "slice" op, so we keep python's
  +# We override the 'slice' for the "slice" op, so we keep Python's
  # existing 'slice' for later use in this module.
  _BaseSlice = slice
  @@ -296,7 +296,7 @@ def expand_dims(input, axis=None, name=None, dim=None):
  """Returns a tensor with a length 1 axis inserted at index `axis`.
  Given a tensor `input`, this operation inserts a dimension of length 1 at the
  - dimension index `axis` of `input`'s shape. The dimension index follows python
  + dimension index `axis` of `input`'s shape. The dimension index follows Python
  indexing rules: It's zero-based, a negative index it is counted backward
  from the end.
  @@ -322,14 +322,14 @@ def expand_dims(input, axis=None, name=None, dim=None):
  >>> tf.expand_dims(image, axis=1).shape.as_list()
  [10, 1, 10, 3]
  - Following standard python indexing rules, a negative `axis` counts from the
  + Following standard Python indexing rules, a negative `axis` counts from the
  end so `axis=-1` adds an inner most dimension:
  >>> tf.expand_dims(image, -1).shape.as_list()
  [10, 10, 3, 1]
  This operation requires that `axis` is a valid index for `input.shape`,
  - following python indexing rules:
  + following Python indexing rules:
  ```
  -1-tf.rank(input) <= axis <= tf.rank(input)
  @@ -368,7 +368,7 @@ def expand_dims_v2(input, axis, name=None):
  """Returns a tensor with a length 1 axis inserted at index `axis`.
  Given a tensor `input`, this operation inserts a dimension of length 1 at the
  - dimension index `axis` of `input`'s shape. The dimension index follows python
  + dimension index `axis` of `input`'s shape. The dimension index follows Python
  indexing rules: It's zero-based, a negative index it is counted backward
  from the end.
  @@ -394,14 +394,14 @@ def expand_dims_v2(input, axis, name=None):
  >>> tf.expand_dims(image, axis=1).shape.as_list()
  [10, 1, 10, 3]
  - Following standard python indexing rules, a negative `axis` counts from the
  + Following standard Python indexing rules, a negative `axis` counts from the
  end so `axis=-1` adds an inner most dimension:
  >>> tf.expand_dims(image, -1).shape.as_list()
  [10, 10, 3, 1]
  This operation requires that `axis` is a valid index for `input.shape`,
  - following python indexing rules:
  + following Python indexing rules:
  ```
  -1-tf.rank(input) <= axis <= tf.rank(input)
  @@ -1072,7 +1072,7 @@ def strided_slice(input_,
  shrink_axis_mask=0,
  var=None,
  name=None):
  - """Extracts a strided slice of a tensor (generalized python array indexing).
  + """Extracts a strided slice of a tensor (generalized Python array indexing).
  See also `tf.slice`.
  @@ -2220,7 +2220,7 @@ def matrix_transpose(a, name="matrix_transpose", conjugate=False):
  # If we know the number of dimensions (statically), we can do two things:
  # 1. Check that `a` is a (batch) matrix.
  - # 2. Use a python list for perm. This preserves static shape information
  + # 2. Use a Python list for perm. This preserves static shape information
  # and avoids extra computations.
  a_shape = a.get_shape()
  ndims = a_shape.ndims
  