message: Update docstring to remove confusing term num_split/size_split, based on review
  Also removes unnecessary `...`
diff: @@ -1919,27 +1919,25 @@ def split(value, num_or_size_splits, axis=0, num=None, name="split"):
  See also `tf.unstack`.
  - If `num_or_size_splits` is an integer, we call it num_split and
  - `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 an integer, then `value` is split along the
  + dimension `axis` into `num_or_size_splits` smaller tensors. This requires that
  + `value.shape[axis]` is divisible by `num_or_size_splits`.
  - 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
  + If `num_or_size_splits` is a 1-D Tensor (or list), then `value` is split into
  + `len(num_or_size_splits)` elements. The shape of the `i`-th
  element has the same size as the `value` except along dimension `axis` where
  - the size is `size_splits[i]`.
  + the size is `num_or_size_splits[i]`.
  For example:
  >>> x = tf.Variable(tf.random.uniform([5, 30], -1, 1))
  >>>
  - >>> # Split `x` into 3 tensors along dimension 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)
  