message: Add runnable examples for `tf.sparse.to_dense`
diff: @@ -111,6 +111,15 @@ def from_dense(tensor, name=None):
  Only elements not equal to zero will be present in the result. The resulting
  `SparseTensor` has the same dtype and shape as the input.
  + >>> sp = tf.sparse.from_dense([0, 0, 3, 0, 1])
  + >>> sp.shape.as_list()
  + [5]
  + >>> sp.values.numpy()
  + array([3, 1], dtype=int32)
  + >>> sp.indices.numpy()
  + array([[2],
  + [4]])
  +
  Args:
  tensor: A dense `Tensor` to be converted to a `SparseTensor`.
  name: Optional name for the op.
  @@ -1602,23 +1611,24 @@ def sparse_tensor_to_dense(sp_input,
  name=None):
  """Converts a `SparseTensor` into a dense tensor.
  - This op is a convenience wrapper around `sparse_to_dense` for `SparseTensor`s.
  -
  - For example, if `sp_input` has shape `[3, 5]` and non-empty string values:
  + For this sparse tensor with three non-empty values:
  - [0, 1]: a
  - [0, 3]: b
  - [2, 0]: c
  + >>> sp_input = tf.SparseTensor(
  + ... dense_shape=[3, 5],
  + ... values=[7, 8, 9],
  + ... indices =[[0, 1],
  + ... [0, 3],
  + ... [2, 0]])
  - and `default_value` is `x`, then the output will be a dense `[3, 5]`
  - string tensor with values:
  + The output will be a dense `[3, 5]` tensor with values:
  - [[x a x b x]
  - [x x x x x]
  - [c x x x x]]
  + >>> tf.sparse.to_dense(sp_input).numpy()
  + array([[0, 7, 0, 8, 0],
  + [0, 0, 0, 0, 0],
  + [9, 0, 0, 0, 0]], dtype=int32)
  - Indices must be without repeats. This is only
  - tested if `validate_indices` is `True`.
  + Note: Indices must be without repeats. This is only tested if
  + `validate_indices` is `True`.
  Args:
  sp_input: The input `SparseTensor`.
  