// 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

                     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],

  let summary = [{
Gradient op for `MirrorPad` op. This op folds a mirror-padded tensor.
  }];

  let description = [{
This operation folds the padded areas of `input` by `MirrorPad` according to the
`paddings` you specify. `paddings` must be the same as `paddings` argument
given to the corresponding `MirrorPad` op.

The folded size of each dimension D of the output is: