message: shorten tf.shape docstring
  clarify when it's different from `x.shape`
diff: @@ -535,19 +535,16 @@ def shape_v2(input, out_type=dtypes.int32, name=None):
  # pylint: disable=redefined-builtin
  """Returns the shape of a tensor.
  - This operation returns a 1-D integer tensor representing the shape of `input`.
  - This represents the minimal set of known information at definition time.
  + `tf.shape` returns a 1-D integer tensor representing the shape of `input`.
  For example:
  >>> t = tf.constant([[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]])
  >>> tf.shape(t)
  <tf.Tensor: shape=(3,), dtype=int32, numpy=array([2, 2, 3], dtype=int32)>
  - >>> tf.shape(t).numpy()
  - array([2, 2, 3], dtype=int32)
  - Note: When using symbolic tensors, such as when using the Keras functional
  - API, tf.shape() will return the shape of the symbolic tensor.
  + Note: When using symbolic tensors, such as when using the Keras API,
  + tf.shape() will return the shape of the symbolic tensor.
  >>> a = tf.keras.layers.Input((None, 10))
  >>> tf.shape(a)
  @@ -558,17 +555,12 @@ def shape_v2(input, out_type=dtypes.int32, name=None):
  >>> a.shape
  TensorShape([None, None, 10])
  - However, when defining custom layers and models that will be run in graph mode
  - at some point, prefer `tf.shape(x)` over `x.shape`. `x.shape` is the static shape
  - of `x` and usually evaluates to `None` in the first dimension during graph
  - construction (to represent the as yet unknown batch size). This can cause problems in
  - function calls like `tf.zeros(x.shape[0])` which don't support `None` values.
  - `tf.shape(x)` on the other hand gives the dynamic shape of `x` which isn't
  - evaluated until training/predicting begins where the full shape of `x` is known.
  + (The first `None` represents the as yet unknown batch size.)
  `tf.shape` and `Tensor.shape` should be identical in eager mode. Within
  `tf.function` or within a `compat.v1` context, not all dimensions may be
  - known until execution time.
  + known until execution time. Hence when defining custom layers and models
  + for graph mode, prefer the dynamic `tf.shape(x)` over the static `x.shape`.
  Args:
  input: A `Tensor` or `SparseTensor`.
  