code: def evaluate_embeds(
      features,
      labels,
      train_mask,
      test_mask,
      n_classes,
      cuda,
      test_times=10,
  ):
      print(
          "Training a logistic regression classifier with the pre-defined train/test split setting ..."
      )
      res_list = []
      for _ in range(test_times):
          model = LogisticRegressionClassifier(
              nfeat=features.shape[1], nclass=n_classes
          )
          if cuda:
              model.cuda()
          res = _train_test_with_lrc(
              model=model,
              features=features,
              labels=labels,
              train_mask=train_mask,
              test_mask=test_mask,
          )
          res_list.append(res)
      return mean(res_list)
  
file_path: dgl/examples/pytorch/rect/classify.py
imports: from statistics import mean
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 3 for calling a global function with example usage: evaluate_embeds(features, labels, train_mask, test_mask, n_classes, cuda, test_times) and returns: mean