if (inference_type == QUANTIZED_INT8) {
      all_device_inference_types.push_back({device, QUANTIZED_INT8});
    } else if (inference_type == QUANTIZED_UINT8) {
      all_device_inference_types.push_back({device, QUANTIZED_UINT8});
    }

    // We will alway enable float.
    all_device_inference_types.push_back({device, FLOAT});
  }

  return all_device_inference_types;
}

          outputs_replaced.push_back(user.getResult());
          output_types.push_back(user.getType());
        } else if (isa<IntegerType>(result_element_type)) {
          // If the result is an integer tensor, then it doesn't require the
          // dequantize op in the pattern.
          outputs_replaced.push_back(result);
          output_types.push_back(result.getType());
        } else {
          return;

    if (rhs_batch_idx.contains(i)) {
      if (rhs_only_batch) {
        rhs_eq.push_back(c);
        lhs_batch_dims.push_back(c);
        c++;
      } else {
        rhs_eq.push_back(lhs_batch_dims[batch_trace_idx]);
        batch_trace_idx++;
      }
    } else if (rhs_contract_idx.contains(i)) {
      absl::StrAppend(&rhs_eq,
                      std::string(1, lhs_contract_dims[contract_trace_idx]));

          rmax = std::max(std::abs(rmin), std::abs(rmax));
          rmin = -rmax;
        }
        TensorRangeSanityCheck(op, rmin, rmax);
        mins.push_back(rmin);
        maxs.push_back(rmax);
      }
      quant_type = quantfork::fakeQuantAttrsToType(
          op.getLoc(), num_bits, *op.getAxis(), mins, maxs, narrow_range,
          expressed, is_signed);

              op->getLoc(), unconstrainted_tensor_type, op->getOperand(i));
          variadic_operands.push_back(casted);
        }
        auto build_list_op = builder.create<BuildListOp>(
            op->getLoc(), list_type, variadic_operands);
        new_operands.push_back(build_list_op.getOut());
      } else {
        auto attr_name = compose_func.getArgAttrOfType<StringAttr>(

    result_types.push_back(op->getOpResult(2).getType());

    // Populate attributes.
    SmallVector<NamedAttribute, 4> attributes;
    // Activation will always be tanh.
    attributes.push_back(rewriter.getNamedAttr("fused_activation_function",
                                               rewriter.getStringAttr("TANH")));
    // cell_clip.
    attributes.push_back(

  std::vector<std::string> disabled_checks;
  for (auto attr : op.getDisabledChecks().getAsRange<StringAttr>()) {
    disabled_checks.push_back(attr.getValue().str());
  }
  std::vector<std::string> platforms;
  for (auto attr : op.getPlatforms().getAsRange<StringAttr>()) {
    platforms.push_back(attr.getValue().str());
  }
  TF_ASSIGN_OR_RETURN(
      auto loader,
      tensorflow::XlaCallModuleLoader::Create(

  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());

  std::vector<TensorHandlePtr> vector_handles;
  vector_handles.reserve(2);
  vector_handles.push_back(std::move(two_vector));
  vector_handles.push_back(std::move(three_vector));
  std::unique_ptr<ParallelTensor> unknown_length_vector =
      ParallelTensor::FromTensorHandles(
          parallel_device, std::move(vector_handles), status.get());

  } else {
    SmallVector<double> scales_vec;
    SmallVector<int64_t> zero_points_vec;
    for (auto elem : scales.getValues<float>()) scales_vec.push_back(elem);
    for (auto elem : zero_points.getValues<int32_t>())
      zero_points_vec.push_back(elem);
    elem_ty = quant::UniformQuantizedPerAxisType::get(
        flags, storage_type, expressed_type, scales_vec, zero_points_vec,

                      arg_locs);
  SmallVector<NamedAttribute> func_attrs;
  func_attrs.push_back(
      {StringAttr::get(context, "inputs"),
       StringAttr::get(context, absl::StrJoin(input_names, ","))});
  func_attrs.push_back(
      {StringAttr::get(context, "outputs"),
       StringAttr::get(context, absl::StrJoin(output_names, ","))});