old_contents: import unittest
  
  from deepchem.feat import MolGraphConvFeaturizer
  from deepchem.models import GATModel
  from deepchem.models.tests.test_graph_models import get_dataset
  
  try:
    import torch  # noqa
    import torch_geometric  # noqa
    has_pytorch_and_pyg = True
  except:
    has_pytorch_and_pyg = False
  
  
  @unittest.skipIf(not has_pytorch_and_pyg,
                   'PyTorch and PyTorch Geometric are not installed')
  def test_gat_regression():
    # load datasets
    featurizer = MolGraphConvFeaturizer()
    tasks, dataset, transformers, metric = get_dataset(
        'regression', featurizer=featurizer)
  
    # initialize models
    n_tasks = len(tasks)
    model = GATModel(
        mode='regression', n_tasks=n_tasks, batch_size=4, learning_rate=0.001)
  
    # overfit test
    # GAT's convergence is a little slow
    model.fit(dataset, nb_epoch=300)
    scores = model.evaluate(dataset, [metric], transformers)
    assert scores['mean_absolute_error'] < 0.2
  
  
  @unittest.skipIf(not has_pytorch_and_pyg,
                   'PyTorch and PyTorch Geometric are not installed')
  def test_gat_classification():
    # load datasets
    featurizer = MolGraphConvFeaturizer()
    tasks, dataset, transformers, metric = get_dataset(
        'classification', featurizer=featurizer)
  
    # initialize models
    n_tasks = len(tasks)
    model = GATModel(
        mode='classification',
        n_tasks=n_tasks,
        batch_size=10,
        learning_rate=0.001)
  
    # overfit test
    # GAT's convergence is a little slow
    model.fit(dataset, nb_epoch=150)
    scores = model.evaluate(dataset, [metric], transformers)
    assert scores['mean-roc_auc_score'] >= 0.9
  
new_contents: import unittest
  
  from deepchem.feat import MolGraphConvFeaturizer
  from deepchem.models import GATModel
  from deepchem.models.tests.test_graph_models import get_dataset
  
  try:
    import torch  # noqa
    import torch_geometric  # noqa
    has_pytorch_and_pyg = True
  except:
    has_pytorch_and_pyg = False
  
  
  @unittest.skipIf(not has_pytorch_and_pyg,
                   'PyTorch and PyTorch Geometric are not installed')
  def test_gat_regression():
    # load datasets
    featurizer = MolGraphConvFeaturizer()
    tasks, dataset, transformers, metric = get_dataset(
        'regression', featurizer=featurizer)
  
    # initialize models
    n_tasks = len(tasks)
    model = GATModel(mode='regression', n_tasks=n_tasks, batch_size=10)
  
    # overfit test
    # GAT's convergence is a little slow
    model.fit(dataset, nb_epoch=300)
    scores = model.evaluate(dataset, [metric], transformers)
    assert scores['mean_absolute_error'] < 0.5
  
  
  @unittest.skipIf(not has_pytorch_and_pyg,
                   'PyTorch and PyTorch Geometric are not installed')
  def test_gat_classification():
    # load datasets
    featurizer = MolGraphConvFeaturizer()
    tasks, dataset, transformers, metric = get_dataset(
        'classification', featurizer=featurizer)
  
    # initialize models
    n_tasks = len(tasks)
    model = GATModel(
        mode='classification',
        n_tasks=n_tasks,
        batch_size=10,
        learning_rate=0.001)
  
    # overfit test
    # GAT's convergence is a little slow
    model.fit(dataset, nb_epoch=150)
    scores = model.evaluate(dataset, [metric], transformers)
    assert scores['mean-roc_auc_score'] >= 0.9
  
subject: fix ci error