code: def testDistributedTrainer():
      import torch
      from deepchem.models.torch_models import GCNModel
      from deepchem.models.trainer import DistributedTrainer
  
      train_smiles = [
          "C1CCC1",
          "CCC",
          "C1CCC1",
          "CCC",
          "C1CCC1",
          "CCC",
          "C1CCC1",
          "CCC",
          "C1CCC1",
          "CCC",
      ]
      train_labels = [0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0]
  
      model = GCNModel(
          mode="regression",
          n_tasks=1,
          batch_size=2,
          learning_rate=0.001,
          device=torch.device("cpu"),
      )
      featurizer = dc.feat.MolGraphConvFeaturizer()
      X = featurizer.featurize(train_smiles)
      dataset = dc.data.NumpyDataset(X=X, y=train_labels)
  
      trainer = DistributedTrainer(
          max_epochs=1, batch_size=64, accelerator="cpu"
      )
      trainer.fit(model, dataset)
  
label_desc: Use image node_id 1 for calling a global function with example usage: testDistributedTrainer() without return types