code: def test_weave_model():
      tasks, dataset, transformers, metric = get_dataset(
          "classification", "Weave", data_points=10
      )
  
      batch_size = 10
      model = WeaveModel(
          len(tasks),
          batch_size=batch_size,
          mode="classification",
          dropouts=0,
          learning_rate=0.0001,
      )
      model.fit(dataset, nb_epoch=250)
      scores = model.evaluate(dataset, [metric], transformers)
      assert scores["mean-roc_auc_score"] >= 0.9
  
file_path: deepchem/deepchem/models/tests/test_weave_models.py
imports: import numpy
  import pytest
  from flaky import flaky
  from deepchem.data import NumpyDataset
  from deepchem.metrics import Metric, roc_auc_score, mean_absolute_error
  from deepchem.molnet import load_bace_classification, load_delaney
  from deepchem.feat import WeaveFeaturizer
label_desc: Use image node_id 4 for calling a global function with example usage: test_weave_model() without return types