(MHLO_FftOp $input, MHLO_FftTypeValue<"FFT">, (GetInnerDimFromValue $res)),
          [(CheckInnerDimStatic $input)]>;

def : Pat<(TF_IFFTOp:$res $input),
          (MHLO_FftOp $input, MHLO_FftTypeValue<"IFFT">, (GetInnerDimFromValue $res)),
          [(CheckInnerDimStatic $input)]>;

//===----------------------------------------------------------------------===//
// GatherV2 op patterns.

      "FakeQuantWithMinMaxVarsPerChannel",
      "FakeQuantWithMinMaxVarsPerChannelGradient",
      "Gather",
      "GatherNd",
      "GatherV2",
      "HSVToRGB",
      "IFFT",
      "IFFT2D",
      "IFFT3D",
      "IRFFT",
      "IRFFT2D",
      "IRFFT3D",
      "If",
      "InTopKV2",
      "L2Loss",
      "LeakyRelu",
      "LinSpace",

// CHECK-LABEL: func @ifft_1D
func.func @ifft_1D(%arg0: tensor<8xcomplex<f32>>) -> tensor<8xcomplex<f32>> {
  // CHECK: "mhlo.fft"(%arg0) <{fft_length = dense<8> : tensor<1xi64>, fft_type = #mhlo<fft_type IFFT>}> : (tensor<8xcomplex<f32>>
  %0 = "tf.IFFT"(%arg0) : (tensor<8xcomplex<f32>>) -> tensor<8xcomplex<f32>>
  func.return %0 : tensor<8xcomplex<f32>>
}

// -----

// CHECK-LABEL: func @rfft_1D

      mlir::RankedTensorType::get({},
      $_builder.getType<mlir::TF::ResourceType>()),
      container, shared_name, use_node_name_sharing, key_dtype, value_dtype);
    }]>];
}

def TF_IFFTOp : TF_Op<"IFFT", [Pure]> {
  let summary = "Inverse fast Fourier transform.";

  let description = [{
Computes the inverse 1-dimensional discrete Fourier transform over the
inner-most dimension of `input`.
  }];

        object (e.g. "hparams.add_hparam(name='a.b', value=0.0)").
    *   Benchmark for tf.scan in graph and eager modes.
    *   Added complex128 support to FFT, FFT2D, FFT3D, IFFT, IFFT2D, and IFFT3D.
    *   Making ids unique in `nn.embedding_lookup_sparse`. This helps to reduce
        RPC calls for looking up the embeddings when there are repeated ids in
        the batch.