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.