code: def test_weave_fit_simple_distance_1():
      featurizer = WeaveFeaturizer(max_pair_distance=1)
      X = featurizer(["C", "CCC"])
      y = np.array([0, 1.0])
      dataset = NumpyDataset(X, y)
  
      batch_size = 20
      model = WeaveModel(
          1,
          batch_size=batch_size,
          mode="classification",
          fully_connected_layer_sizes=[2000, 1000],
          batch_normalize=True,
          batch_normalize_kwargs={
              "fused": False,
              "trainable": True,
              "renorm": True,
          },
          learning_rate=0.0005,
      )
      model.fit(dataset, nb_epoch=200)
      transformers = []
      metric = Metric(roc_auc_score, np.mean, mode="classification")
      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 6 for calling a global function with example usage: test_weave_fit_simple_distance_1() without return types