input_ = tf.raw_ops.Concat(
        concat_dim=i, values=[left_padding, input_, right_padding])
  return input_


@tf.RegisterGradient('NewMirrorPad')
def _mirror_pad_grad(op, grad):
  mode = op.get_attr('mode')
  return [gen_array_ops.mirror_pad_grad(grad, op.inputs[1], mode=mode), None]


@Composite(
    'NewMirrorPadGrad',
    inputs=['input_: T', 'paddings: Tpaddings'],
    attrs=['mode: {"REFLECT", "SYMMETRIC"}'],

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

  TensorShape y_shape({2, 3});
  auto y = MirrorPadGrad(scope_, x, paddings, "REFLECT");
  RunTest(x, x_shape, y, y_shape);
}

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

REGISTER_GRADIENT_OP("DepthToSpace", DepthToSpaceGrad);

Status MirrorPadGrad(const Scope& scope, const Operation& op,
                     const std::vector<Output>& grad_inputs,
                     std::vector<Output>* grad_outputs) {
  string mode;
  TF_RETURN_IF_ERROR(GetNodeAttr(op.node()->attrs(), "mode", &mode));
  grad_outputs->push_back(tensorflow::ops::internal::MirrorPadGrad(
      scope, grad_inputs[0], op.input(1), mode));

      "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",
      "ResourceStridedSliceAssign", "Tile", "Transpose", "InvertPermutation",

*   Fixes an overflow in `CONV_3D_TRANSPOSE` on TFLite ([CVE-2022-41894](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41894))
*   Fixes a heap OOB in `MirrorPadGrad` ([CVE-2022-41895](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41895))
*   Fixes a crash in `Mfcc` ([CVE-2022-41896](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41896))

  );

  TF_DerivedOperandTypeAttr T = TF_DerivedOperandTypeAttr<0>;
  TF_DerivedOperandTypeAttr Tpaddings = TF_DerivedOperandTypeAttr<1>;
}

def TF_MirrorPadGradOp : TF_Op<"MirrorPadGrad", [Pure, TF_OperandHasRank<1, 2>]> {
  let summary = [{
Gradient op for `MirrorPad` op. This op folds a mirror-padded tensor.
  }];

  let description = [{