%4 = "tf.Case"(%2, %arg0, %arg1) {branches = [@sub, @add], output_shapes = [#tf_type.shape<>], device = "noodle", is_stateless = false} : (tensor<i32>, tensor<f32>, tensor<f32>) -> tensor<f32>
  // CHECK: PartitionedCall
  // CHECK-SAME: f = @sub
  // CHECK-SAME: _cluster_launch = "not_ready"
  // CHECK-SAME: device = "noodle"

                .getScale(),
            /*filter_scales=*/filter_quantized_type.getScales());
        const ArrayRef<int64_t> output_shape =
            op->getResult(0).getType().cast<TensorType>().getShape();
        const SmallVector<int64_t, 1> bias_shape = {
            output_shape[output_shape.size() - 1]};
        // `tfl.fully_connected`'s `GetChannelDimIndex` is 0.
        const auto bias_quantized_type =

      %arg1: tensor<i64>
    ) {
      %1 = "tf.ReduceDataset"(%arg0, %arg1) {
        Targuments = [],
        Tstate = [i64], device = "",
        f = @__reduce_func_1, f._tf_data_function = true,
        output_shapes = [#tf_type.shape<>],
        output_types = [i64], use_inter_op_parallelism = true, _xla_compile_device_type="TPU"} :
          (tensor<!tf_type.variant>, tensor<i64>) -> (tensor<i64>)
      func.return
    }
    ```

  let summary = "Transpose convolution operator";

  let description = [{
    Performs transpose convolution operation on input.
  }];

  let arguments = (ins
    TFL_I32Tensor:$output_shape,
    TFL_TensorOf<[F32, QI8, QUI8, QI16]>:$weights,
    TFL_TensorOf<[F32, QI8, QUI8, QI16]>:$input,
    TFL_TensorOfOrNone<[F32, QI32, I64]>:$bias,
    TFL_PaddingAttr:$padding,

                    IsWithinInt32Range);

    if (elements_all_in_int32_range) {
      std::vector<int32_t> output_shape(output_ty.getRank());
      std::transform(output_ty.getShape().begin(), output_ty.getShape().end(),
                     output_shape.begin(),
                     [](int64_t val) { return static_cast<int32_t>(val); });
      output_int_type = tensorflow::GetTypeFromTFTensorShape(

      device = "/job:tpu_host_worker/replica:0/task:0/device:CPU:0",
      finalize_func = @finalize,
      init_func = @init,
      next_func = @next,
      operandSegmentSizes = array<i32: 1, 2, 1>,
      output_shapes = [#tf_type.shape<>],
      output_types = [!tf_type.string],
      metadata = ""} : (
              tensor<4xf32>,
              tensor<3xf32>,
              tensor<!tf_type.resource>,