code: def patch_module(
      module: torch.nn.Module,
      layers: Sequence,
      weight_dropout: float = 0.0,
      inplace: bool = True,
  ) -> torch.nn.Module:
      """
      Replace given layers with weight_drop module of that layer.
  
      Args:
          module : torch.nn.Module
              The module in which you would like to replace dropout layers.
          layers : list[str]
              Name of layers to be replaced from ['Conv', 'Linear', 'LSTM', 'GRU'].
          weight_dropout (float): The probability a weight will be dropped.
          inplace : bool, optional
              Whether to modify the module in place or return a copy of the module.
  
      Returns:
          torch.nn.Module:
              The modified module, which is either the same object as you passed in
              (if inplace = True) or a copy of that object.
      """
      return _patching_wrapper(
          module,
          inplace=inplace,
          patching_fn=_dropconnect_mapping_fn,
          layers=layers,
          weight_dropout=weight_dropout,
      )
  
imports: import copy
  import warnings
  from typing import List, Optional, cast, Dict
  from baal.bayesian.common import replace_layers_in_module, _patching_wrapper, BayesianModule
  import torch
  from torch import nn
  from packaging.version import parse
label_desc: Use image node_id 2 for calling a global function with example usage: patch_module(module, layers, weight_dropout, inplace) and returns: _patching_wrapper