code: def test_multitask_classifier(self):
      class TestMultitaskDatasetBatch:
          def __init__(self, batch):
              X = [batch[0]]
              y = [
                  np.array(
                      [
                          to_one_hot(b.flatten(), 2).reshape(2, 2)
                          for b in batch[1]
                      ]
                  )
              ]
              w = [batch[2]]
              self.batch_list = [X, y, w]
  
      def collate_dataset_wrapper(batch):
          return TestMultitaskDatasetBatch(batch)
  
      tasks, datasets, _ = dc.molnet.load_clintox()
      _, valid_dataset, _ = datasets
  
      model = MultitaskClassifier(
          n_tasks=len(tasks),
          n_features=1024,
          layer_sizes=[1000],
          dropouts=0.2,
          learning_rate=0.0001,
      )
  
      molnet_dataloader = DCLightningDatasetModule(
          valid_dataset, 6, collate_dataset_wrapper
      )
      lightning_module = DCLightningModule(model)
      trainer = L.Trainer(max_epochs=1)
      trainer.fit(lightning_module, molnet_dataloader)
  
imports: import unittest
  import deepchem
  import numpy
label_desc: Use image node_id 1 for calling the TestDCLightningModule obj's underlying member method code with example usage: obj.test_multitask_classifier() and returns: TestMultitaskDatasetBatch