code: def _create_large_module():
      def _conv2d():
          return torch.nn.Conv2d(8, 8, (3, 3), bias=False, groups=1)
  
      return torch.nn.Sequential(
          OrderedDict(
              [
                  ("conv1", _conv2d()),
                  ("conv2", _conv2d()),
                  ("conv3", _conv2d()),
                  ("flatten", torch.nn.Flatten()),
                  ("linear1", torch.nn.Linear(2592, 100)),
                  ("linear2", torch.nn.Linear(100, 10)),
              ]
          )
      )
  
imports: import copy
  from collections import OrderedDict
  import numpy
  import pytest
  import torch
  from coremltools.optimize.torch.pruning import MagnitudePruner, MagnitudePrunerConfig, ModuleMagnitudePrunerConfig
  from coremltools.optimize.torch.pruning._utils import n_m_mask
label_desc: Use image node_id 4 for calling a global function with example usage: _create_large_module() and returns: torch, torch