message: Updated docs for `tf.fill()`.
diff: @@ -210,32 +210,34 @@ def fill(dims, value, name=None):
  For example:
  - ```
  - # Output tensor has shape [2, 3].
  - fill([2, 3], 9) ==> [[9, 9, 9]
  - [9, 9, 9]]
  - ```
  -
  - `tf.fill` differs from `tf.constant` in a few ways:
  + # Output tensor with shape [2, 3].
  + >>> tf.fill([2, 3], 9)
  + <tf.Tensor: shape=(2, 3), dtype=int32, numpy=
  + array([[9, 9, 9],
  + [9, 9, 9]], dtype=int32)>
  - * `tf.fill` only supports scalar contents, whereas `tf.constant` supports
  - Tensor values.
  - * `tf.fill` creates an Op in the computation graph that constructs the
  - actual
  - Tensor value at runtime. This is in contrast to `tf.constant` which embeds
  - the entire Tensor into the graph with a `Const` node.
  - * Because `tf.fill` evaluates at graph runtime, it supports dynamic shapes
  - based on other runtime Tensors, unlike `tf.constant`.
  + `tf.fill` evaluates at graph runtime and supports dynamic shapes based on
  + other runtime `tf.Tensors`, unlike `tf.constant(value, shape=dims)`, which
  + embeds the value as a `Const` node.
  Args:
  - dims: A `Tensor`. Must be one of the following types: `int32`, `int64`. 1-D.
  - Represents the shape of the output tensor.
  - value: A `Tensor`. 0-D (scalar). Value to fill the returned tensor.
  - @compatibility(numpy) Equivalent to np.full @end_compatibility
  - name: A name for the operation (optional).
  + dims: A 1-D sequence of non-negative numbers. Represents the shape of the
  + output `tf.Tensor`. Entries should be of type: `int32`, `int64`.
  + value: A value to fill the returned `tf.Tensor`.
  + name: Optional string. The name of the output `tf.Tensor`.
  Returns:
  - A `Tensor`. Has the same type as `value`.
  + A `tf.Tensor` with shape `dims` and the same dtype as `value`.
  +
  + Raises:
  + InvalidArgumentError: `dims` contains negative entries.
  + NotFoundError: `dims` contains non-integer entries.
  +
  + @compatibility(numpy)
  + Similar to `np.full`. In `numpy`, more parameters are supported. Passing a
  + number argument as the shape (`np.full(5, value)`) is valid in `numpy` for
  + specifying a 1-D shaped result, while TensorFlow does not support this syntax.
  + @end_compatibility
  """
  result = gen_array_ops.fill(dims, value, name=name)
  tensor_util.maybe_set_static_shape(result, dims)
  