code: def test_weave_regression_model():
      np.random.seed(22)
      torch.manual_seed(22)
      tasks, dataset, transformers, metric = get_dataset(
          "regression", "Weave", data_points=2
      )
      # Note: Following are some changes
      # compared to the TensorFlow unit test:
      # 1. Changed nb_epoch to 400.
      # 2. Reduced the number of data points to 2.
      # 3. Increased batch_size to 20.
      # 4. Increased the learning_rate to 0.0003.
      batch_size = 20
      weave_model = WeaveModel(
          len(tasks),
          batch_size=batch_size,
          mode="regression",
          dropouts=0,
          learning_rate=0.0003,
      )
      weave_model.fit(dataset, nb_epoch=400)
      scores = weave_model.evaluate(dataset, [metric], transformers)
      assert scores["mean_absolute_error"] < 0.1
  
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 7 for calling a global function with example usage: test_weave_regression_model() without return types