TF_RETURN_IF_ERROR(GetNodeAttr(op.node()->attrs(), "seq_dim", &seq_dim));
  grad_outputs->push_back(
      ReverseSequence(scope, grad_inputs[0], seq_lengths, seq_dim,
                      ReverseSequence::BatchDim(batch_dim)));
  grad_outputs->push_back(NoGradient());
  return scope.status();
}
REGISTER_GRADIENT_OP("ReverseSequence", ReverseSequenceGrad);

Status ReverseGrad(const Scope& scope, const Operation& op,

  }

  // CHECK-LABEL: reverse_sequence
  func.func @reverse_sequence(%arg0: tensor<4x2x3x1x1xi32>, %arg1: tensor<3xi32>) -> tensor<4x2x3x1x1xi32> {
    // CHECK-NOT: tf.ReverseSequence
    %0 = "tf.ReverseSequence"(%arg0, %arg1) {batch_dim = 2 : i64, seq_dim = 0 : i64}: (tensor<4x2x3x1x1xi32>, tensor<3xi32>) -> tensor<4x2x3x1x1xi32>
    func.return %0 : tensor<4x2x3x1x1xi32>
  }

  // CHECK-LABEL: mirror_pad

  TensorShape shape({5, 2, 5});
  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape));
  auto seq_lengths = Const(scope_, {1, 2, 3, 4, 5});
  // batch_dim defaults to 0.
  auto y = ReverseSequence(scope_, x, seq_lengths, /* seq_dim */ 2);
  RunTest(x, shape, y, shape);
}

TEST_F(ArrayGradTest, ReverseGrad) {
  TensorShape shape({5, 2, 5});