code: def _create_module():
      conv2d = torch.nn.Conv2d(
          in_channels=3,
          out_channels=4,
          kernel_size=(3, 3),
          bias=False,
          groups=1,
      )
      conv2d.weight = torch.nn.Parameter(
          _mock_initializer(conv2d.weight.shape, np.float32)
      )
      activation = torch.nn.ReLU()
      return torch.nn.Sequential(
          OrderedDict([("conv2d", conv2d), ("activation", activation)])
      )
  
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 3 for calling a global function with example usage: _create_module() and returns: torch