// CHECK:            %[[D_OUTPUT:[0-9]*]] = "tf.D"(%[[C_OUTPUT]])
    // CHECK:            %[[E_OUTPUT:[0-9]*]]:2 = "tf.E"(%[[D_OUTPUT]])
    // CHECK:            tf_device.return %[[D_OUTPUT:[0-9]*]], %[[E_OUTPUT:[0-9]*]]#0, %[[E_OUTPUT:[0-9]*]]#1
    // CHECK:          "tf_device.cluster"
    // CHECK:            %[[A_OUTPUT:[0-9]*]] = "tf.A"

        std::vector<tensorflow::Output> cond_output;
        TF_RETURN_IF_ERROR(CopyGraph(
            &params->cond_graph->graph, &parent->graph, &parent->refiner,
            params->cond_inputs, inputs, scope.impl()->name(),
            scope.impl()->control_deps(), &params->cond_output,
            /* nreturn_nodes */ 1, &cond_output));
        *output = cond_output[0];
        return absl::OkStatus();
      };

  int64_t time =
      time_majored ? input_type.getDimSize(0) : input_type.getDimSize(1);
  int64_t n_output = output_state_type.getDimSize(1);

  // Build the output shape.
  SmallVector<int64_t, 3> output_shape;
  if (time_majored) {
    output_shape = {time, batch, n_output};
  } else {
    output_shape = {batch, time, n_output};
  }
  auto result_type = tensorflow::GetTypeFromTFTensorShape(

// Fuse Mul with proceeding Affine ops. This is an C++ implementation of the
// following table gen implementation, which doesn't derived the result type of
// the TFL_DequantizeOp.
// def : Pat<(TFL_MulOp (TFL_Conv2DOp:$conv_output $input,
//                          (TFL_DequantizeOp (TFL_QuantizeOp
//                              (Arith_ConstantOp F32ElementsAttr:$filter),
//                              $qtype)),

  // result of the new operation, and deletes the old operation.
  for (unsigned i = 0, e = dst->getNumResults(); i != e; ++i) {
    auto new_output = new_dst->getResult(i);
    dst->getResult(i).replaceAllUsesWith(new_output);
  }
  dst->dropAllReferences();
  dst->erase();
  return absl::OkStatus();
}

absl::StatusOr<mlir::FunctionType> ImporterBase::InferLibFunctionType(