code: def test_multi_modal_real_model(use_gpu):
      class CustomModule(torch.nn.Module):
          def __init__(self):
              super().__init__()
              self.linear1 = torch.nn.Linear(1, 1)
              self.linear2 = torch.nn.Linear(1, 1)
  
          def forward(self, input_dict: dict):
              # Add feature dimension, expanding (batch_size,) to (batch_size, 1).
              input_dict["A"] = input_dict["A"].unsqueeze(1)
              input_dict["B"] = input_dict["B"].unsqueeze(1)
              out1 = self.linear1(input_dict["A"])
              out2 = self.linear2(input_dict["B"])
              return out1 + out2
  
      predictor = TorchPredictor(model=CustomModule(), use_gpu=use_gpu)
  
      data = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"])
  
      predictions = predictor.predict(data, dtype=torch.float)
      assert len(predictions) == 2
      if use_gpu:
          assert next(
              predictor.model.parameters()
          ).is_cuda, "Model should be moved to GPU if use_gpu is True"
      else:
          assert not next(
              predictor.model.parameters()
          ).is_cuda, "Model should not be on GPU if use_gpu is False"
  
imports: import re
  import numpy
  import pandas
  import pytest
  import torch
  from ray.air.constants import MAX_REPR_LENGTH
  from ray.air.util.data_batch_conversion import _convert_batch_type_to_pandas, _convert_pandas_to_batch_type
  from ray.train.predictor import TYPE_TO_ENUM
  from ray.train.tests.dummy_preprocessor import DummyPreprocessor
  from ray.train.torch import TorchCheckpoint, TorchPredictor
label_desc: Use image node_id 15 for calling a global function with example usage: test_multi_modal_real_model(use_gpu) and returns: unknown