TF::RFFT2DOp rfft2d = rewriter.create<TF::RFFT2DOp>(
        rfft_op.getLoc(), rfft2d_out_type, expanded_input.getResult(),
        expanded_fft_len.getResult());

    // Insert the squeeze op.
    auto squeeze_dim = rewriter.getI64ArrayAttr({-2});
    TF::SqueezeOp squeeze = rewriter.create<TF::SqueezeOp>(
        rfft_op.getLoc(), output_type, rfft2d.getResult(), squeeze_dim);

}

// -----

func.func @rfft2d(%arg0: tensor<10x20x10x30xf32>, %arg1: tensor<2xi32>) -> tensor<10x20x10x30xcomplex<f32>> {
  %0 = "tf.RFFT2D"(%arg0, %arg1) : (tensor<10x20x10x30xf32>, tensor<2xi32>) -> tensor<10x20x10x30xcomplex<f32>>
  func.return %0 : tensor<10x20x10x30xcomplex<f32>>
  // CHECK-LABEL: rfft2d

  SCATTER_ND = 122,
  SELECT_V2 = 123,
  DENSIFY = 124,
  SEGMENT_SUM = 125,
  BATCH_MATMUL = 126,
  PLACEHOLDER_FOR_GREATER_OP_CODES = 127,
  CUMSUM = 128,
  CALL_ONCE = 129,
  BROADCAST_TO = 130,
  RFFT2D = 131,
  CONV_3D = 132,
  IMAG=133,
  REAL=134,
  COMPLEX_ABS=135,
  HASHTABLE = 136,
  HASHTABLE_FIND = 137,
  HASHTABLE_IMPORT = 138,
  HASHTABLE_SIZE = 139,
  REDUCE_ALL = 140,

  SCATTER_ND = 122,
  SELECT_V2 = 123,
  DENSIFY = 124,
  SEGMENT_SUM = 125,
  BATCH_MATMUL = 126,
  PLACEHOLDER_FOR_GREATER_OP_CODES = 127,
  CUMSUM = 128,
  CALL_ONCE = 129,
  BROADCAST_TO = 130,
  RFFT2D = 131,
  CONV_3D = 132,
  IMAG=133,
  REAL=134,
  COMPLEX_ABS=135,
  HASHTABLE = 136,
  HASHTABLE_FIND = 137,
  HASHTABLE_IMPORT = 138,
  HASHTABLE_SIZE = 139,
  REDUCE_ALL = 140,

  let hasCanonicalizer = 1;
}

def TFL_RFFT2dOp : TFL_Op<"rfft2d", [Pure]> {
  let summary = "2D real-valued fast Fourier transform.";

  let description = [{
Computes the 2-dimensional discrete Fourier transform of a real-valued signal
over the inner-most 2 dimensions of `input`.

Since the DFT of a real signal is Hermitian-symmetric, `RFFT2D` only returns the

// CHECK:  %[[CON:.*]] = "tf.ConcatV2"(%[[CST0]], %arg1, %[[CST1]]) : (tensor<1xi32>, tensor<1xi32>, tensor<i32>) -> tensor<2xi32>
// CHECK:  %[[RFF:.*]] = "tf.RFFT2D"(%[[EXP]], %[[CON]]) : (tensor<10x20x1x30xf32>, tensor<2xi32>) -> tensor<10x20x1x30xcomplex<f64>>
// CHECK:  %[[SQE:.*]] = "tf.Squeeze"(%[[RFF]]) <{squeeze_dims = [-2]}> : (tensor<10x20x1x30xcomplex<f64>>) -> tensor<10x20x30xcomplex<f64>>

      "Polygamma",
      "PopulationCount",
      "Qr",
      "QuantizeAndDequantizeV2",
      "QuantizeAndDequantizeV3",
      "QuantizeAndDequantizeV4",
      "RFFT",
      "RFFT2D",
      "RFFT3D",
      "RGBToHSV",
      "RandomShuffle",
      "RandomStandardNormal",
      "RandomUniform",
      "RandomUniformInt",
      "ReadVariableOp",
      "ReadVariableXlaSplitND",

}

def TF_RFFT2DOp : TF_Op<"RFFT2D", [Pure]> {
  let summary = "2D real-valued fast Fourier transform.";

  let description = [{
Computes the 2-dimensional discrete Fourier transform of a real-valued signal
over the inner-most 2 dimensions of `input`.

Since the DFT of a real signal is Hermitian-symmetric, `RFFT2D` only returns the

    *   Added dynamic range quantization support for the BatchMatMul op.
        *   Both symmetric and asymmetric quantized input tensor are supported.
    *   Add `RFFT2D` as builtin op. (`RFFT2D` also supports `RFFTD`.) Currently
        only supports float32 input.
    *   Add 5D support to `SLICE` op.
    *   TFLite Supports SingatureDef:
        *   TFLiteConverter exports models with SignatureDef

    "SCATTER_ND",
    "SELECT_V2",
    "DENSIFY",
    "SEGMENT_SUM",
    "BATCH_MATMUL",
    "PLACEHOLDER_FOR_GREATER_OP_CODES",
    "CUMSUM",
    "CALL_ONCE",
    "BROADCAST_TO",
    "RFFT2D",
    "CONV_3D",
    "IMAG",
    "REAL",
    "COMPLEX_ABS",
    "HASHTABLE",
    "HASHTABLE_FIND",
    "HASHTABLE_IMPORT",
    "HASHTABLE_SIZE",
    "REDUCE_ALL",