code: def _mock_initializer(shape, dtype):
      # Each output channel is (entirely) an integer, increasing. This makes it so
      # that we know what to expect from the LnPruner.
      output_channel_index = 0
      num_output_channels = shape[output_channel_index]
      output_channel_values = np.arange(
          1, num_output_channels + 1, dtype=dtype
      )
      broadcast_shape = tuple(
          -1 if i == output_channel_index else 1
          for i, _ in enumerate(shape)
      )
      output_channel_values_reshaped = np.reshape(
          output_channel_values, broadcast_shape
      )
      return torch.tensor(
          np.broadcast_to(output_channel_values_reshaped, shape)
      )
  
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 2 for calling a global function with example usage: _mock_initializer(shape, dtype) and returns: torch