code: def __init__(self, iC: int, oC: List[int]):
      super().__init__()
      self.linears = nn.Sequential()
      i = iC
      for idx, c in enumerate(oC):
          self.linears.append(nn.Linear(i, c, bias=False))
          if idx < len(oC) - 1:
              self.linears.append(nn.ReLU())
          i = c
  
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 to create a new DummyModel object from inherited base classes: nn with example: obj = DummyModel(iC, oC)