def _BiasAddGrad(op, received_grad):
  """Return the gradients for the 2 inputs of bias_op.

  The first input of unused_bias_op is the tensor t, and its gradient is
  just the gradient the unused_bias_op received.

  The second input of unused_bias_op is the bias vector which has one fewer
  dimension than "received_grad" (the batch dimension.)  Its gradient is the
  received gradient Summed on the batch dimension, which is the first dimension.

  Args:
    op: The BiasOp for which we need to generate gradients.
    received_grad: Tensor.  The gradients passed to the BiasOp.

  Returns:
    Two tensors, the first one for the "tensor" input of the BiasOp,
    the second one for the "bias" input of the BiasOp.
  """
  try:
    data_format = op.get_attr("data_format")
  except ValueError:
    data_format = None
  return (received_grad,
          gen_nn_ops.bias_add_grad(
              out_backprop=received_grad, data_format=data_format))

  def testBiasAddGrad(self):
    self._testUnary(
        gen_nn_ops.bias_add_grad,
        np.array([[1., 2.], [3., 4.]], dtype=np.float32),
        expected=np.array([4., 6.], dtype=np.float32))

    self._testUnary(lambda x: gen_nn_ops.bias_add_grad(x, data_format="NCHW"),
                    np.array([[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]]],
                             dtype=np.float32),
                    expected=np.array([10., 26.], dtype=np.float32))

  def testBiasAddGrad(self):
    self._assertOpOutputMatchesExpected(
        gen_nn_ops.bias_add_grad,
        np.array([[1., 2.], [3., 4.]], dtype=np.float32),
        expected=np.array([4., 6.], dtype=np.float32))

    self._assertOpOutputMatchesExpected(
        lambda x: gen_nn_ops.bias_add_grad(x, data_format="NCHW"),
        np.array(
            [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]]], dtype=np.float32),
        expected=np.array([14., 22.], dtype=np.float32))