code: def test_megnet_overfit():
      fgg = FGG(
          avg_n_nodes=10,
          n_node_features=5,
          avg_degree=4,
          n_edge_features=3,
          global_features=4,
          num_classes=5,
          task="graph",
      )
      graphs = fgg.sample(n_graphs=100)
  
      model = MEGNetModel(
          n_node_features=5,
          n_edge_features=3,
          n_global_features=4,
          n_blocks=3,
          is_undirected=True,
          residual_connection=True,
          mode="classification",
          n_classes=5,
          batch_size=16,
      )
      metric = dc.metrics.Metric(
          dc.metrics.accuracy_score, mode="classification"
      )
  
      model.fit(graphs, nb_epoch=100)
      scores = model.evaluate(graphs, [metric], n_classes=5)
      assert scores["accuracy_score"] == 1.0
  
file_path: deepchem/deepchem/models/tests/test_megnet.py
imports: import pytest
  import tempfile
  import numpy
  import deepchem
  from deepchem.utils.fake_data_generator import FakeGraphGenerator
label_desc: Use image node_id 1 for calling a global function with example usage: test_megnet_overfit() without return types