code: def test_trainer_predict_verify_config(tmpdir, datamodule):
      class TestModel(LightningModule):
          def __init__(self):
              super().__init__()
              self.layer = torch.nn.Linear(32, 2)
  
          def forward(self, x):
              return self.layer(x)
  
      class TestLightningDataModule(LightningDataModule):
          def __init__(self, dataloaders):
              super().__init__()
              self._dataloaders = dataloaders
  
          def test_dataloader(self):
              return self._dataloaders
  
          def predict_dataloader(self):
              return self._dataloaders
  
      data = [
          torch.utils.data.DataLoader(RandomDataset(32, 2)),
          torch.utils.data.DataLoader(RandomDataset(32, 2)),
      ]
      if datamodule:
          data = TestLightningDataModule(data)
  
      model = TestModel()
      trainer = Trainer(default_root_dir=tmpdir)
      results = trainer.predict(model, data)
  
      assert len(results) == 2
      assert results[0][0].shape == torch.Size([1, 2])
  
file_path: pytorch-lightning/tests/tests_pytorch/trainer/test_config_validator.py
imports: from unittest.mock import Mock
  import pytest
  import torch
  from lightning.fabric.utilities.warnings import PossibleUserWarning
  from lightning.pytorch import LightningDataModule, LightningModule, Trainer
  from lightning.pytorch.demos.boring_classes import BoringModel, RandomDataset
  from lightning.pytorch.trainer.configuration_validator import __verify_eval_loop_configuration, __verify_train_val_loop_configuration
  from lightning.pytorch.utilities.exceptions import MisconfigurationException
label_desc: Use image node_id 5 for calling a global function with example usage: test_trainer_predict_verify_config(tmpdir, datamodule) and returns: self, self, self