def testBroadcast_one_dimension(self):
    s1 = tensor_shape.vector(5)
    s2 = tensor_shape.vector(7)

    unknown = tensor_shape.unknown_shape()
    scalar = tensor_shape.scalar()
    expanded_scalar = tensor_shape.TensorShape([1])

    # Tensors with same shape should have the same broadcast result.
    for shape in (s1, s2, unknown, scalar, expanded_scalar):
      self._assert_broadcast(expected=shape, shape1=shape, shape2=shape)

    # [] and [1] act like identity.
    self._assert_broadcast(expected=s1, shape1=s1, shape2=scalar)
    self._assert_broadcast(expected=s2, shape1=s2, shape2=scalar)
    self._assert_broadcast(expected=s1, shape1=s1, shape2=expanded_scalar)
    self._assert_broadcast(expected=s2, shape1=s2, shape2=expanded_scalar)

    self._assert_broadcast(expected=unknown, shape1=s1, shape2=unknown)
    self._assert_broadcast(expected=unknown, shape1=s2, shape2=unknown)

    self._assert_broadcast(
        expected=expanded_scalar, shape1=scalar, shape2=expanded_scalar)

    self._assert_incompatible_broadcast(shape1=s1, shape2=s2)

def _TensorArraySplitShape(op):
    # handle, value, lengths, flow_in
    op.inputs[0].get_shape().merge_with(tensor_shape.vector(2))
    op.inputs[2].get_shape().merge_with(tensor_shape.vector(None))
    op.inputs[3].get_shape().merge_with(tensor_shape.scalar())
    # flow_out
    return [tensor_shape.scalar()]

  def testShapes(self):
    fdef = self._build_function_def()

    g = function_def_to_graph.function_def_to_graph(fdef)
    self.assertIsNone(g.inputs[0].shape.dims)  # Unknown dims.
    self.assertIsNone(g.inputs[1].shape.dims)  # Unknown dims.
    self.assertIsNone(g.outputs[0].shape.dims)  # Unknown dims.
    self.assertIsNone(g.outputs[1].shape.dims)  # Unknown dims.

    g = function_def_to_graph.function_def_to_graph(
        fdef, input_shapes=[tensor_shape.vector(5),
                            tensor_shape.vector(5)])
    self.assertSequenceEqual(g.inputs[0].shape.dims, [5])
    self.assertSequenceEqual(g.inputs[1].shape.dims, [5])
    self.assertSequenceEqual(g.outputs[0].shape.dims, [5])
    self.assertSequenceEqual(g.outputs[1].shape.dims, [5])

    g = function_def_to_graph.function_def_to_graph(
        fdef, input_shapes=[None, tensor_shape.matrix(5, 7)])
    self.assertIsNone(g.inputs[0].shape.dims)
    self.assertSequenceEqual(g.inputs[1].shape.dims, [5, 7])
    self.assertSequenceEqual(g.outputs[0].shape.dims, [5, 7])
    self.assertSequenceEqual(g.outputs[1].shape.dims, [5, 7])

    # Should raise a ValueError if the length of input_shapes does not match
    # the number of input args in FunctionDef.signature.input_arg.
    with self.assertRaises(ValueError):
      g = function_def_to_graph.function_def_to_graph(
          fdef, input_shapes=[tensor_shape.matrix(5, 7)])

  def testBroadcast_one_dimension(self):
    s1 = tensor_shape.vector(5)
    s2 = tensor_shape.vector(7)

    unknown = tensor_shape.unknown_shape()
    scalar = tensor_shape.scalar()
    expanded_scalar = tensor_shape.TensorShape([1])

    # Tensors with same shape should have the same broadcast result.
    self.assertEqual(s1, common_shapes.broadcast_shape(s1, s1))
    self.assertEqual(s2, common_shapes.broadcast_shape(s2, s2))
    self.assertEqual(unknown, common_shapes.broadcast_shape(unknown, unknown))
    self.assertEqual(scalar, common_shapes.broadcast_shape(scalar, scalar))
    self.assertEqual(expanded_scalar, common_shapes.broadcast_shape(
        expanded_scalar, expanded_scalar))

    # [] acts like an identity.
    self.assertEqual(s1, common_shapes.broadcast_shape(s1, scalar))
    self.assertEqual(s2, common_shapes.broadcast_shape(s2, scalar))

    self.assertEqual(s1, common_shapes.broadcast_shape(s1, expanded_scalar))
    self.assertEqual(s2, common_shapes.broadcast_shape(s2, expanded_scalar))

    self.assertEqual(unknown, common_shapes.broadcast_shape(s1, unknown))
    self.assertEqual(unknown, common_shapes.broadcast_shape(s2, unknown))

    self.assertEqual(expanded_scalar, common_shapes.broadcast_shape(
        scalar, expanded_scalar))

    with self.assertRaises(ValueError):
      common_shapes.broadcast_shape(s1, s2)
      common_shapes.broadcast_shape(s2, s1)

def _ParseSingleSequenceExampleShape(op):
    """Shape function for the ParseExample op."""
    op.inputs[0].get_shape().with_rank(0)  # input
    # feature_list_dense_missing_assumed_empty
    op.inputs[1].get_shape().with_rank(1)
    num_context_sparse = op.get_attr("Ncontext_sparse")
    num_context_dense = op.get_attr("Ncontext_dense")
    num_feature_list_dense = op.get_attr("Nfeature_list_dense")
    context_dense_shapes = op.get_attr("context_dense_shapes")
    num_feature_list_sparse = op.get_attr("Nfeature_list_sparse")
    feature_list_dense_shapes = op.get_attr("feature_list_dense_shapes")
    context_sparse_index_shapes = [tensor_shape.matrix(None, 1) for _ in range(num_context_sparse)]
    context_sparse_value_shapes = [tensor_shape.vector(None) for _ in range(num_context_sparse)]
    context_sparse_shape_shapes = [tensor_shape.vector(1) for _ in range(num_context_sparse)]
    context_dense_shapes = [tensor_shape.TensorShape(dense_shape) for dense_shape in context_dense_shapes]
    feature_list_sparse_index_shapes = [tensor_shape.matrix(None, 2) for _ in range(num_feature_list_sparse)]
    feature_list_sparse_value_shapes = [tensor_shape.vector(None) for _ in range(num_feature_list_sparse)]
    feature_list_sparse_shape_shapes = [tensor_shape.vector(2) for _ in range(num_feature_list_sparse)]
    feature_list_dense_shapes = [
        tensor_shape.vector(None).concatenate(dense_shape) for dense_shape in feature_list_dense_shapes
    ]
    assert num_context_dense == len(context_dense_shapes)
    assert num_feature_list_dense == len(feature_list_dense_shapes)
    return (
        context_sparse_index_shapes
        + context_sparse_value_shapes
        + context_sparse_shape_shapes
        + context_dense_shapes
        + feature_list_sparse_index_shapes
        + feature_list_sparse_value_shapes
        + feature_list_sparse_shape_shapes
        + feature_list_dense_shapes
    )

def _CandidateSamplerShape(op):
  true_classes_shape = op.inputs[0].get_shape().with_rank(2)
  batch_size = true_classes_shape[0]
  num_sampled = op.get_attr("num_sampled")
  num_true = op.get_attr("num_true")
  return [tensor_shape.vector(num_sampled),
          tensor_shape.matrix(batch_size, num_true),
          tensor_shape.vector(num_sampled)]

def _RangeShape(op):
    start_value = tensor_util.constant_value(op.inputs[0])
    limit_value = tensor_util.constant_value(op.inputs[1])
    delta_value = tensor_util.constant_value(op.inputs[2])
    if start_value is None or limit_value is None or delta_value is None:
        return [tensor_shape.vector(None)]
    else:
        return [tensor_shape.vector((limit_value - start_value + delta_value - 1) // delta_value)]

def _SparseSoftmaxCrossEntropyWithLogitsShape(op):
  """Shape function for SparseSoftmaxCrossEntropyWithLogits op."""
  logits_shape = op.inputs[0].get_shape()
  input_shape = logits_shape.with_rank(2)
  batch_size = input_shape[0]
  # labels_shape
  op.inputs[1].get_shape().merge_with(tensor_shape.vector(batch_size))
  return [tensor_shape.vector(batch_size.value), input_shape]

def _DeserializeSparseShape(op):  # pylint: disable=invalid-name
  """Shape function for DeserializeManySparse op."""
  serialized_sparse_shape = op.inputs[0].get_shape().with_rank(2)
  serialized_sparse_shape.merge_with(
      tensor_shape.TensorShape([None, 3]))

  return [tensor_shape.matrix(None, None),
          tensor_shape.vector(None),
          tensor_shape.vector(None)]

def _SaveSlicesShape(op):
    """Shape function for SaveSlices op."""
    # Validate input shapes.
    unused_filename = op.inputs[0].get_shape().merge_with(tensor_shape.scalar())
    data_count = len(op.inputs) - 3
    unused_tensor_names_shape = op.inputs[1].get_shape().merge_with(tensor_shape.vector(data_count))
    unused_shapes_and_slices_shape = op.inputs[2].get_shape().merge_with(tensor_shape.vector(data_count))
    # TODO(mrry): Attempt to parse the shapes_and_slices values and use
    # them to constrain the shape of the remaining inputs.
    return []

def _ParseExampleShape(op):
    """Shape function for the ParseExample op."""
    input_shape = op.inputs[0].get_shape().with_rank(1)
    op.inputs[1].get_shape().with_rank(1)  # names
    num_sparse = op.get_attr("Nsparse")
    num_dense = op.get_attr("Ndense")
    dense_shapes = op.get_attr("dense_shapes")
    sparse_index_shapes = [tensor_shape.matrix(None, 2) for _ in range(num_sparse)]
    sparse_value_shapes = [tensor_shape.vector(None) for _ in range(num_sparse)]
    sparse_shape_shapes = [tensor_shape.vector(2) for _ in range(num_sparse)]
    assert num_dense == len(dense_shapes)
    dense_shapes = [input_shape.concatenate(dense_shape) for dense_shape in dense_shapes]
    return sparse_index_shapes + sparse_value_shapes + sparse_shape_shapes + dense_shapes

def _CTCGreedyDecoderShape(op):
  """Shape function for the CTCGreedyDecoder op."""
  inputs_shape = op.inputs[0].get_shape().with_rank(3)
  sequence_length_shape = op.inputs[1].get_shape().with_rank(1)
  # merge batch_size
  sequence_length_shape[0].merge_with(inputs_shape[1])
  inputs_shape[1].merge_with(sequence_length_shape[0])
  batch_size = inputs_shape[1]
  # decoded_indices, decoded_values, decoded_shape, log_probability
  return [tensor_shape.matrix(None, 2),
          tensor_shape.vector(None),
          tensor_shape.vector(2),
          tensor_shape.matrix(batch_size, 1)]

  def testBroadcast_unknown_dims(self):
    unknown = tensor_shape.unknown_shape()
    shape_0 = tensor_shape.scalar()
    shape_1 = tensor_shape.vector(1)
    # pylint: disable=invalid-name
    shape_U = tensor_shape.vector(None)
    shape_1xU = tensor_shape.matrix(1, None)
    shape_Ux1 = tensor_shape.matrix(None, 1)
    shape_4xU = tensor_shape.matrix(4, None)
    shape_Ux4 = tensor_shape.matrix(None, 4)
    # pylint: enable=invalid-name

    # Tensors with same shape should have the same broadcast result.
    for shape in (shape_U, shape_1xU, shape_Ux1, shape_4xU, shape_Ux4):
      self._assert_broadcast_with_unknown_dims(
          expected=shape, shape1=shape, shape2=shape)

    # [] and [1] act like identity.
    for identity in (shape_0, shape_1):
      for shape in (shape_U, shape_1xU, shape_Ux1, shape_4xU, shape_Ux4):
        self._assert_broadcast_with_unknown_dims(
            expected=shape, shape1=identity, shape2=shape)

    # Unknown in, unknown out.
    for shape in (shape_U, shape_1xU, shape_Ux1, shape_4xU, shape_Ux4):
      self._assert_broadcast_with_unknown_dims(
          expected=unknown, shape1=shape, shape2=unknown)

    self._assert_broadcast_with_unknown_dims(
        expected=shape_1xU, shape1=shape_U, shape2=shape_1xU)
    shape_UxU = tensor_shape.matrix(None, None)  # pylint: disable=invalid-name
    self._assert_broadcast_with_unknown_dims(
        expected=shape_UxU, shape1=shape_U, shape2=shape_Ux1)
    self._assert_broadcast_with_unknown_dims(
        expected=shape_4xU, shape1=shape_U, shape2=shape_4xU)
    self._assert_broadcast_with_unknown_dims(
        expected=shape_Ux4, shape1=shape_U, shape2=shape_Ux4)
    self._assert_broadcast_with_unknown_dims(
        expected=shape_UxU, shape1=shape_1xU, shape2=shape_Ux1)
    self._assert_broadcast_with_unknown_dims(
        expected=shape_4xU, shape1=shape_1xU, shape2=shape_4xU)
    self._assert_broadcast_with_unknown_dims(
        expected=shape_Ux4, shape1=shape_1xU, shape2=shape_Ux4)
    self._assert_broadcast_with_unknown_dims(
        expected=shape_4xU, shape1=shape_Ux1, shape2=shape_4xU)
    self._assert_broadcast_with_unknown_dims(
        expected=shape_Ux4, shape1=shape_Ux1, shape2=shape_Ux4)
    shape_4x4 = tensor_shape.matrix(4, 4)
    self._assert_broadcast_with_unknown_dims(
        expected=shape_4x4, shape1=shape_4xU, shape2=shape_Ux4)

  def sample(self, n, seed=None, name="sample"):
    """Sample `n` observations from the Uniform Distributions.

    Args:
      n: `Scalar`, type int32, the number of observations to sample.
      seed: Python integer, the random seed.
      name: The name to give this op.

    Returns:
      samples: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
          with values of type `self.dtype`.
    """
    with ops.name_scope(self.name):
      with ops.op_scope([self.a, self.b, n], name):
        n = ops.convert_to_tensor(n, name="n")
        n_val = tensor_util.constant_value(n)

        shape = array_ops.concat(0, [array_ops.pack([n]), self.batch_shape()])
        samples = random_ops.random_uniform(shape=shape,
                                            dtype=self.dtype,
                                            seed=seed)

        # Provide some hints to shape inference
        inferred_shape = tensor_shape.vector(n_val).concatenate(
            self.get_batch_shape())
        samples.set_shape(inferred_shape)

        return (array_ops.expand_dims(self.a, 0) + array_ops.expand_dims(
            self.range(), 0) * samples)

def _TransposeShape(op):
  """Shape function for the Transpose op.

  This op takes two inputs:

  * input: a rank-N tensor of arbitrary shape.
  * shuffle: a length-N vector.

  Its output is the rank-N tensor computed by permuting the dimensions
  of input according to shuffle.

  Args:
    op: A Transpose op.

  Returns:
    A single-element list containing the shape of the output.

  Raises:
    ValueError: If the shapes of input and shuffle are incompatible.
    IndexError: If shuffle contains an index that is >= the rank of input.
  """
  input_shape = op.inputs[0].get_shape()
  transpose_shape = op.inputs[1].get_shape().merge_with(tensor_shape.vector(
      input_shape.ndims))
  transpose_vec = tensor_util.ConstantValue(op.inputs[1])
  if transpose_vec is None:
    return [tensor_shape.unknown_shape(ndims=transpose_shape[0].value)]
  else:
    return [tensor_shape.TensorShape([input_shape[i]
                                      for i in transpose_vec.tolist()])]

  def sample(self, n, seed=None, name="sample"):
    """Sample `n` observations from the Categorical distribution.

    Args:
      n: 0-D.  Number of independent samples to draw for each distribution.
      seed: Random seed (optional).
      name: A name for this operation (optional).

    Returns:
      An `int64` `Tensor` with shape `[n, batch_shape, event_shape]`
    """
    with ops.name_scope(self.name):
      with ops.op_scope([self.logits, n], name):
        n = ops.convert_to_tensor(n, name="n")
        logits_2d = array_ops.reshape(
            self.logits, array_ops.pack([-1, self.num_classes]))
        samples = random_ops.multinomial(logits_2d, n, seed=seed)
        samples = math_ops.cast(samples, self._dtype)
        ret = array_ops.reshape(
            array_ops.transpose(samples),
            array_ops.concat(
                0, [array_ops.expand_dims(n, 0), self.batch_shape()]))
        ret.set_shape(tensor_shape.vector(tensor_util.constant_value(n))
                      .concatenate(self.get_batch_shape()))
        return ret

  def sample_n(self, n, seed=None, name="sample_n"):
    """Sample `n` observations from the Beta Distributions.

    Args:
      n: `Scalar` `Tensor` of type `int32` or `int64`, the number of
        observations to sample.
      seed: Python integer, the random seed.
      name: The name to give this op.

    Returns:
      samples: `[n, ...]`, a `Tensor` of `n` samples for each
        of the distributions determined by broadcasting the hyperparameters.
    """
    with ops.name_scope(self.name):
      with ops.name_scope(name, values=[self.a, self.b, n]):
        a = array_ops.ones_like(self._a_b_sum, dtype=self.dtype) * self.a
        b = array_ops.ones_like(self._a_b_sum, dtype=self.dtype) * self.b
        n = ops.convert_to_tensor(n, name="n")

        gamma1_sample = random_ops.random_gamma(
            [n,], a, dtype=self.dtype, seed=seed)
        gamma2_sample = random_ops.random_gamma(
            [n,], b, dtype=self.dtype, seed=seed)

        beta_sample = gamma1_sample / (gamma1_sample + gamma2_sample)

        n_val = tensor_util.constant_value(n)
        final_shape = tensor_shape.vector(n_val).concatenate(
            self._a_b_sum.get_shape())

        beta_sample.set_shape(final_shape)
        return beta_sample

 def testHelpers(self):
   tensor_shape.TensorShape([]).assert_is_compatible_with(
       tensor_shape.scalar())
   tensor_shape.TensorShape([37]).assert_is_compatible_with(
       tensor_shape.vector(37))
   tensor_shape.TensorShape(
       [94, 43]).assert_is_compatible_with(tensor_shape.matrix(94, 43))

def _SerializeSparseShape(op):  # pylint: disable=invalid-name
  """Shape function for SerializeSparse op."""
  op.inputs[0].get_shape().with_rank(2)
  op.inputs[1].get_shape().with_rank(1)
  op.inputs[2].get_shape().with_rank(1)

  return [tensor_shape.vector(3)]

 def _from_tensor_list(self, flat_value):
   if (len(flat_value) != 1 or flat_value[0].dtype != dtypes.variant or
       not flat_value[0].shape.is_compatible_with(tensor_shape.vector(3))):
     raise ValueError("SparseTensorStructure corresponds to a single "
                      "tf.variant vector of length 3.")
   return sparse_ops.deserialize_sparse(
       flat_value[0], dtype=self._dtype, rank=self._dense_shape.ndims)