code: def _make_lightning_module(iC: int, oC: List[int]):
      import pytorch_lightning as pl  # type: ignore[import]
  
      class DummyLightningModule(pl.LightningModule):
          def __init__(self, ic: int, oC: List[int]):
              super().__init__()
              self.model = DummyModel(iC, oC)
  
          def forward(self):
              pass
  
      return DummyLightningModule(iC, oC)
  
imports: from torch.ao.pruning._experimental.data_sparsifier.data_norm_sparsifier import DataNormSparsifier
  from torch.ao.pruning._experimental.data_scheduler.base_data_scheduler import BaseDataScheduler
  import torch
  import torch.nn
  from typing import List
  from torch.ao.pruning._experimental.data_sparsifier.lightning.callbacks.data_sparsity import PostTrainingDataSparsity, TrainingAwareDataSparsity
  from torch.ao.pruning._experimental.data_sparsifier.lightning.callbacks._data_sparstity_utils import _get_valid_name
  from torch.ao.pruning._experimental.data_sparsifier.base_data_sparsifier import SUPPORTED_TYPES
  from torch.testing._internal.common_utils import TestCase
  from torch.testing._internal.common_utils import run_tests
  import importlib
  import unittest
  import warnings
  import math
  from torch.nn.utils.parametrize import is_parametrized
label_desc: Use image node_id 1 for calling a global function with example usage: _make_lightning_module(iC, oC) and returns: DummyLightningModule