%0 = mhlo.constant dense<[[1, 1], [2, 2]]> : tensor<2x2xi32>
  // NO_FALLBACK: tf.MirrorPad
  // SUPPORTED_FALLBACK_DEVICE-NOT: tf.MirrorPad
  // UNSPECIFIED_FALLBACK_DEVICE: tf.MirrorPad
  // UNSUPPORTED_FALLBACK_DEVICE: tf.MirrorPad
  %1 = "tf.MirrorPad"(%arg0, %0) {mode = "SYMMETRIC"} : (tensor<2x3xcomplex<f64>>, tensor<2x2xi32>) -> tensor<4x7xcomplex<f64>>
  func.return %1 : tensor<4x7xcomplex<f64>>

    "MatrixDiag",
    "MatrixDiagPart",
    "MatrixDiagPartV2",
    "MatrixDiagPartV3",
    "MatrixDiagV2",
    "MatrixDiagV3",
    "MatrixSetDiag",
    "MatrixSetDiagV2",
    "MatrixSetDiagV3",
    "MirrorPad",
    "OneHot",
    "OnesLike",
    "Pack",
    "Pad",
    "PadV2",
    "ParallelConcat",
    "PlaceholderV2",
    "PlaceholderWithDefault",
    "PreventGradient",
    "QuantizeAndDequantize",

    }
    kwargs_ = {
        'input_': input_,
        'paddings': paddings,
        'mode': mode,
    }
    # Make sure the composition python function is correct
    self._assertOpAndComposite([input_], tf.raw_ops.MirrorPad,
                               ops_defs._composite_mirror_pad, kwargs_, kwargs)
    # Make sure the translation and decomposition is correct
    self._assertOpAndComposite([input_],

  TensorShape y_shape({4, 7});
  auto y = MirrorPad(scope_, x, paddings, "REFLECT");
  RunTest(x, x_shape, y, y_shape);
}

TEST_F(ArrayGradTest, MirrorPadGrad_Symmetric) {
  TensorShape x_shape({2, 3});
  auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape));
  auto paddings = Const(scope_, {{1, 1}, {2, 2}});
  TensorShape y_shape({4, 7});
  auto y = MirrorPad(scope_, x, paddings, "SYMMETRIC");

  grad_outputs->push_back(tensorflow::ops::internal::MirrorPadGrad(
      scope, grad_inputs[0], op.input(1), mode));
  grad_outputs->push_back(NoGradient());
  return scope.status();
}
REGISTER_GRADIENT_OP("MirrorPad", MirrorPadGrad);

// TODO(suharshs): b/34770860. This gradient was within 1e-3 but not 1e-4.
Status MirrorPadGradGrad(const Scope& scope, const Operation& op,
                         const std::vector<Output>& grad_inputs,

  // CHECK-LABEL: mirror_pad
  func.func @mirror_pad(%arg0: tensor<2x3xcomplex<f64>>) -> tensor<4x7xcomplex<f64>> {
    %0 = mhlo.constant dense<[[1, 1], [2, 2]]> : tensor<2x2xi32>
    // CHECK-NOT: tf.MirrorPad
    %1 = "tf.MirrorPad"(%arg0, %0) {mode = "SYMMETRIC"} : (tensor<2x3xcomplex<f64>>, tensor<2x2xi32>) -> tensor<4x7xcomplex<f64>>
    func.return %1 : tensor<4x7xcomplex<f64>>
  }

  // CHECK-LABEL: bucketize

}

func.func @mirror_pad(tensor<2x1x3xf32>, tensor<3x2xi32>) -> tensor<? x f32> {
^bb0(%arg0: tensor<2x1x3xf32>, %arg1: tensor<3x2xi32>):
  %0 = "tf.MirrorPad"(%arg0, %arg1) { mode = "SYMMETRIC" }: (tensor<2x1x3xf32>, tensor<3x2xi32>) -> tensor<? x f32>
  func.return %0#0 : tensor<? x f32>

  // CHECK-LABEL: mirror_pad

      "Transpose", "ZerosLike", "OnesLike", "BiasAdd" /*PW + Broadcast*/,
      "BroadcastArgs", "BroadcastGradientArgs", "OneHot", "Concat", "ConcatV2",
      "ConcatOffset", "Const", "MirrorPad", "MirrorPadGrad", "Pack", "Pad",
      "PadV2", "Reverse", "ReverseV2", "ReverseSequence", "Slice", "Split",
      "SplitV", "StridedSlice", "StridedSliceGrad",

                     SameOperandsAndResultsScale,
                     QuantizableResult,
                     Pure,
                     TFL_OperandHasRank<1, 2>]> {
  let summary = "MirrorPad Operator. Pads a tensor with mirrored values.";

  let description = [{
    This operation pads a input with mirrored values according to the paddings
    you specify. paddings is an integer tensor with shape [n, 2],

  %paddings = "tf.Const"() {value = dense<[0, 1]> : tensor<2xi64>} : () -> tensor<2xi64>
  // expected-error @+1 {{failed to verify that operand 1 is 2-D}}
  %0 = "tf.MirrorPad"(%input, %paddings) { mode = "SYMMETRIC" }: (tensor<2xi64>, tensor<2xi64>) -> tensor<3xi64>
  func.return %0 : tensor<3xi64>
}

// -----