code: def test_graph_conv_model():
      tasks, dataset, transformers, metric = get_dataset(
          "classification", "GraphConv"
      )
  
      batch_size = 10
      model = GraphConvModel(
          len(tasks),
          batch_size=batch_size,
          batch_normalize=False,
          mode="classification",
      )
  
      model.fit(dataset, nb_epoch=20)
      scores = model.evaluate(dataset, [metric], transformers)
      assert scores["mean-roc_auc_score"] >= 0.9
  
imports: import os
  import numpy
  import pytest
  from scipy import io
  from deepchem.data import NumpyDataset, CSVLoader
  from deepchem.trans import DAGTransformer
  from deepchem.molnet import load_bace_classification, load_delaney
  from deepchem.feat import ConvMolFeaturizer
  from deepchem.metrics import Metric, roc_auc_score, mean_absolute_error
  from deepchem.utils.data_utils import download_url, get_data_dir
  from flaky import flaky
label_desc: Use image node_id 2 for calling a global function with example usage: test_graph_conv_model() without return types