if (sub_op) {
    sub_op->replaceAllUsesWith(island.getOutputs());
    sub_op->moveBefore(block, block->begin());
    island_builder.create<tf_executor::YieldOp>(loc, sub_op->getResults());
  } else {
    island_builder.create<TF::NoOp>(island.getLoc(), TypeRange{}, ValueRange{});
    island_builder.create<tf_executor::YieldOp>(loc, ValueRange{});
  }
  return island;
}

    const Type expressed_type = value.getType();
    // The value needs to be requantized. A Quantize op will be created to use
    // it as the operand and replace its uses.
    const Type new_type = state.params.castFromExpressedType(expressed_type);
    if (!new_type) return;
    auto requantize_op =
        builder_.create<quantfork::QuantizeCastOp>(loc, new_type, value);
    value.replaceAllUsesWith(requantize_op);

  OpBuilder builder(merged_island.insert_point);
  auto new_island = builder.create<IslandOp>(
      merged_island.insert_point->getLoc(), result_types, operands);
  new_island.getBody().push_back(new Block);
  return new_island;
}

// Creates respective YieldOp for the new merged island.
YieldOp CreateNewIslandYieldOp(IslandOp new_island,

// function, which might not exist in case of multi-signature graphs. In that
// case, this pass will create a new main function, which calls signature
// functions.
//
// An already existing @main function will be renamed by attaching a numeric
// suffix like `@main_0` to avoid conflict with the newly created main function.
class InsertMainFunctionPass
    : public PassWrapper<InsertMainFunctionPass, OperationPass<ModuleOp>> {

  auto new_launch_op = builder.create<tf_device::LaunchOp>(
      old_launch_op->getLoc(), old_launch_op->getDeviceAttr(),
      /*result_types=*/new_launch_op_results_types);

  new_launch_op.getBody().takeBody(old_launch_op->getBody());
  new_launch_op.GetBody().getTerminator()->setOperands(new_launch_op_results);

  return new_launch_op;
}

// Create the new device launch op which wraps the copy op.

                                                new_results)))
      return failure();

    // Update the regions. The dialect conversion framework wants new regions to
    // be created and updated, rather than updating the old op. Thus we use an
    // OperationState so we can add regions to the new op.
    OperationState state(op->getLoc(), op->getName().getStringRef(), operands,

      OpBuilder replacer(yield_op);
      island_op.getBody().push_back(new Block);
      replacer.create<mlir::func::ReturnOp>(yield_op.getLoc(),
                                            yield_op.getOperands());
      yield_op.erase();
    }

    // Remap the captured operands in the (former) island block with newly
    // created entry block arguments in the function body.
    {

  // Set insertion point to right after all operations in cluster.
  builder->setInsertionPoint(c.ops.back()->getNextNode());

  // Create a stand-alone region to hold all instructions in the cluster.
  Region region;
  region.push_back(new Block);

  // Move all operations in cluster to newly created region, stripping their
  // "device" attribute since launch op already carries device information.
  Block* block = &region.front();

  ConvertAssetArgsTest() {
    ctx_.loadDialect<func::FuncDialect, TF::TensorFlowDialect,
                     tf_saved_model::TensorFlowSavedModelDialect>();
  }

  // Parses `module_op_str` to create a `ModuleOp`. Checks whether the created
  // module op is valid.
  OwningOpRef<ModuleOp> ParseModuleOpString(
      const absl::string_view module_op_str) {
    auto module_op_ref = parseSourceString<ModuleOp>(module_op_str, &ctx_);

  auto var_handle_op_in_init = var_handle_op->clone();
  builder.insert(var_handle_op_in_init);
  auto const_op = builder.create<mlir::arith::ConstantOp>(
      session_init_func.getLoc(), tensor_attr.getType(), tensor_attr);

  builder.create<TF::AssignVariableOp>(
      session_init_func.getLoc(), llvm::ArrayRef<mlir::Type>{},
      llvm::ArrayRef<mlir::Value>{var_handle_op_in_init->getResult(0),