rewriter.inlineRegionBefore(graph.getNodes(), executor_graph.getBody(),
                                executor_graph.getBody().end());

    // Add terminator of tf_executor::graph
    rewriter.setInsertionPointToEnd(&executor_graph.getBody().front());
    rewriter.create<tf_executor::FetchOp>(loc);

    // Add terminator of func
    rewriter.setInsertionPointToEnd(&func.getBody().front());
    rewriter.create<func::ReturnOp>(loc);

  auto func_op = module_op->lookupSymbol<func::FuncOp>("fully_quantized_add");
  ASSERT_THAT(func_op, NotNull());

  auto add_op_itr = func_op.getBody().op_begin<mlir::stablehlo::AddOp>();
  ASSERT_THAT(add_op_itr,
              Ne(func_op.getBody().op_end<mlir::stablehlo::AddOp>()));

  EXPECT_TRUE(IsOpFullyQuantized(*add_op_itr));
}

TEST_F(IsOpFullyQuantizedTest, FalseIfOpNotQuantized) {

      /*operands=*/llvm::ArrayRef<Value>{}, cluster->getAttrs());
  new_cluster.getBody().takeBody(cluster.getBody());

  auto operand_not_in_cluster = [&](mlir::OpOperand& operand) {
    return !new_cluster.getOperation()->isProperAncestor(operand.getOwner());
  };
  for (auto result :
       llvm::zip(new_cluster.GetBody().getTerminator()->getOperands(),
                 new_cluster.getResults()))

      // and packed args.
      arg.replaceAllUsesWith(
          new_replicate.GetBody().getArgument(arg.getArgNumber() + 1));
    }
  }
  for (auto& op : llvm::make_early_inc_range(replicate.GetBody())) {
    op.moveBefore(&new_replicate.GetBody(), new_replicate.GetBody().end());
  }
  replicate.replaceAllUsesWith(new_replicate);
  replicate.erase();
  return new_replicate;
}

                    IRMapping& mapper) {
  Block& main_body = dst_graph_op.GetBody();

  // Take the reference of the main graph's FetchOp to later move to the end.
  FetchOp main_fetch_op = dst_graph_op.GetFetch();

  Block& save_func_body = src_graph_op.GetBody();
  for (Operation& op : save_func_body.without_terminator()) {
    main_body.push_back(op.clone(mapper));
  }

    type = FunctionType::get(context, types, result_types);
  }

  auto outlined_func = builder.create<func::FuncOp>(loc, name, type);
  outlined_func.getBody().takeBody(region);
  Region& func_region = outlined_func.getBody();

  // Replace all external uses with block args and update uses.
  llvm::SmallVector<Value, 4> new_args;
  new_args.reserve(extern_values.size());

    // Add associated inner op result to operands of the YieldOp.
    yield_operands.push_back(old_result.inner_op_result);
  }

  // Create YieldOp for the new island.
  OpBuilder builder(&new_island.GetBody(), new_island.GetBody().end());
  return builder.create<YieldOp>(new_island.getLoc(), yield_operands);
}

// Moves inner ops (excluding last op/YieldOp) from islands being merged into
// the new merged island.

      /*controlInputs=*/island.getOperands());
  new_island.getBody().push_back(new Block);

  // Move the operations in the new island, gather the results of the new yield.
  Block& island_body = new_island.GetBody();
  SmallVector<Value, 16> yield_operands;
  for (IslandOp island : islands) {
    Operation& wrapped_op = island.GetBody().front();
    wrapped_op.moveBefore(&island_body, island_body.end());

      convert.GetCompositeOpName());
  EXPECT_EQ(fused_lstm_func_.getNumArguments(), 5);
  EXPECT_EQ(fused_lstm_func_.getFunctionType().getNumResults(), 1);

  auto transpose_op = fused_lstm_func_.getBody().front().begin();
  transpose_op++;
  EXPECT_EQ(mlir::cast<RankedTensorType>(transpose_op->getOperand(0).getType())
                .getDimSize(0),
            3);

      new_while_op.getCond().takeBody(while_op.getCond());
      new_while_op.getBody().takeBody(while_op.getBody());
      llvm::BitVector erase_indices;
      UpdateTerminatorArguments(new_while_op.getBody(), args_to_erase,
                                erase_indices);
      llvm::BitVector body_bit_vector(
          new_while_op.getBody().front().getNumArguments());
      for (auto i : args_to_erase) body_bit_vector.set(i);