message: Update doc string for tf.split
diff: @@ -1879,11 +1879,11 @@ unique_with_counts.__doc__ = gen_array_ops.unique_with_counts.__doc__
  @tf_export("split")
  def split(value, num_or_size_splits, axis=0, num=None, name="split"):
  - """Splits a tensor into sub tensors.
  + """Splits a tensor `value` into a list of sub tensors.
  - If `num_or_size_splits` is an integer, then `value` is split along dimension
  - `axis` into `num_split` smaller tensors. This requires that `num_split` evenly
  - divides `value.shape[axis]`.
  + If `num_or_size_splits` is an integer, then `value` is split along the
  + dimension `axis` into `num_split` smaller tensors. This requires that
  + `value.shape[axis]` is divisible by `num_split`.
  If `num_or_size_splits` is a 1-D Tensor (or list), we call it `size_splits`
  and `value` is split into `len(size_splits)` elements. The shape of the `i`-th
  @@ -1892,15 +1892,14 @@ def split(value, num_or_size_splits, axis=0, num=None, name="split"):
  For example:
  - Split `x` into 3 tensors along dimension 1
  -
  >>> x = tf.Variable(tf.random.uniform([5, 30], -1, 1))
  +
  + Split `x` into 3 tensors along dimension 1
  >>> s0, s1, s2 = tf.split(x, num_or_size_splits=3, axis=1)
  >>> tf.shape(s0).numpy()
  array([ 5, 10], dtype=int32)
  Split `x` into 3 tensors with sizes [4, 15, 11] along dimension 1
  -
  >>> split0, split1, split2 = tf.split(x, [4, 15, 11], 1)
  >>> tf.shape(split0).numpy()
  array([5, 4], dtype=int32)
  @@ -1923,8 +1922,8 @@ def split(value, num_or_size_splits, axis=0, num=None, name="split"):
  name: A name for the operation (optional).
  Returns:
  - if `num_or_size_splits` is a scalar returns `num_or_size_splits` `Tensor`
  - objects; if `num_or_size_splits` is a 1-D Tensor returns
  + if `num_or_size_splits` is a scalar returns a list of `num_or_size_splits`
  + `Tensor` objects; if `num_or_size_splits` is a 1-D Tensor returns
  `num_or_size_splits.get_shape[0]` `Tensor` objects resulting from splitting
  `value`.
  