code: def test_dmpnn_model_classification():
      """
      Test DMPNNModel class for classification mode
      """
      torch.manual_seed(0)
  
      # load sample dataset
      dir = os.path.dirname(os.path.abspath(__file__))
      input_file = os.path.join(
          dir, "assets/example_classification.csv"
      )
      loader = dc.data.CSVLoader(
          tasks=["outcome"],
          feature_field="smiles",
          featurizer=dc.feat.DMPNNFeaturizer(),
      )
      dataset = loader.create_dataset(input_file)
  
      # initialize the model
      from deepchem.models.torch_models.dmpnn import DMPNNModel
  
      mode = "classification"
      classes = 2
      tasks = 1
      model = DMPNNModel(
          mode=mode, n_classes=classes, n_tasks=tasks, batch_size=2
      )
  
      # overfit test
      model.fit(dataset, nb_epoch=30)
      metric = dc.metrics.Metric(
          dc.metrics.accuracy_score, mode="classification"
      )
      scores = model.evaluate(dataset, [metric], n_classes=classes)
      assert scores["accuracy_score"] > 0.9
  
file_path: deepchem/deepchem/models/tests/test_dmpnn.py
imports: import pytest
  import deepchem
  import tempfile
  import numpy
  import os
label_desc: Use image node_id 5 for calling a global function with example usage: test_dmpnn_model_classification() without return types