code: def evaluate(eval_ds, model, loss_fn, config, evaluator, dataset):
      model.eval()
      step = 0
      output_metric = defaultdict(lambda: [])
      pred_temp = []
      y_temp = []
      file = "model_result_temp"
      sudo_label = np.memmap(
          file, dtype=np.float32, mode="r+", shape=(121751666, 153)
      )
  
      for batch in tqdm.tqdm(eval_ds.generator()):
          graph_list, x, m2v_x, y, label_y, label_idx, nodes_idx = batch
          x = paddle.to_tensor(x, dtype="float32")
          m2v_x = paddle.to_tensor(m2v_x, dtype="float32")
          y = paddle.to_tensor(y, dtype="int64")
          label_y = paddle.to_tensor(label_y, dtype="int64")
          label_idx = paddle.to_tensor(label_idx, dtype="int64")
  
          graph_list = [
              (item[0].tensor(), paddle.to_tensor(item[2]))
              for item in graph_list
          ]
          out = model(graph_list, x, m2v_x, label_y, label_idx)
          out = F.softmax(out)
          sudo_label[nodes_idx] = out.numpy()
  
      sudo_label.flush()
      model.train()
      return output_metric
  
imports: import os
  import argparse
  import traceback
  import paddle
  import re
  import io
  import tqdm
  import yaml
  import pgl
  import paddle
  import paddle.nn.functional
  import numpy
  import optimization
  from ogb.lsc import MAG240MEvaluator
  from easydict import EasyDict
  from dataset.data_generator_r_unimp_multi_gpu_infer import MAG240M, DataGenerator
  import models
  from pgl.utils.logger import log
  from utils import save_model, infinite_loop, _create_if_not_exist, load_model
  from tensorboardX import SummaryWriter
  from collections import defaultdict
  import time
label_desc: Use image node_id 2 for calling a global function with example usage: evaluate(eval_ds, model, loss_fn, config, evaluator, dataset) and returns: output_metric