from tensorflow.python.ops import gen_nn_ops

    if not output_shape_.get_shape().is_compatible_with(tensor_shape.vector(4)):
      raise ValueError("output_shape must have shape (4,), got {}"
                       .format(output_shape_.get_shape()))

    if isinstance(output_shape, (list, np.ndarray)):
      # output_shape's shape should be == [4] if reached this point.
      if not filter.get_shape()[2].is_compatible_with(output_shape[3]):
        raise ValueError(
            "output_shape does not match filter's output channels, "
            "{} != {}".format(output_shape[3], filter.get_shape()[2]))

    if padding != "VALID" and padding != "SAME":
      raise ValueError("padding must be either VALID or SAME:"
                       " {}".format(padding))

    return gen_nn_ops.conv2d_backprop_input(input_sizes=output_shape_,
                                            filter=filter,
                                            out_backprop=value,
                                            strides=strides,
                                            padding=padding,
                                            name=name)


# pylint: disable=protected-access
def bias_add(value, bias, data_format=None, name=None):
  """Adds `bias` to `value`.

  This is (mostly) a special case of `tf.add` where `bias` is restricted to 1-D.
  Broadcasting is supported, so `value` may have any number of dimensions.
  Unlike `tf.add`, the type of `bias` is allowed to differ from `value` in the
