code: def test_weave_model_reload():
      "Test WeaveModel class for reloading the model."
      torch.manual_seed(0)
      featurizer = WeaveFeaturizer(max_pair_distance=1)
      X = featurizer(["C", "CCC"])
      y = np.array([0, 1.0])
      dataset = NumpyDataset(X, y)
  
      batch_size = 20
      model_dir = tempfile.mkdtemp()
      original_model = WeaveModel(
          1,
          batch_size=batch_size,
          mode="classification",
          fully_connected_layer_sizes=[2000, 1000],
          batch_normalize=True,
          learning_rate=0.0005,
          model_dir=model_dir,
      )
      original_model.fit(dataset, nb_epoch=200)
  
      reloaded_model = WeaveModel(
          1,
          batch_size=batch_size,
          mode="classification",
          fully_connected_layer_sizes=[2000, 1000],
          batch_normalize=True,
          learning_rate=0.0005,
          model_dir=model_dir,
      )
      reloaded_model.restore()
      original_predict = original_model.predict(dataset)
      reloaded_predict = reloaded_model.predict(dataset)
      assert np.all(original_predict == reloaded_predict)
  
file_path: deepchem/deepchem/models/tests/test_weavemodel_pytorch.py
imports: import deepchem
  import numpy
  import os
  import pytest
  import tempfile
  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 8 for calling a global function with example usage: test_weave_model_reload() without return types