code: def _revert(self, module: torch.nn.Module) -> torch.nn.Module:
      if self.module_mapping is not None:
          if "" in self.module_mapping.values():
              for (
                  bt_module_attr_name,
                  value,
              ) in self.module_mapping.items():
                  if value == "":
                      module = getattr(self, bt_module_attr_name)
                      return module
          else:
              raise NotImplementedError(
                  "replacing a submodule in revert is not supported"
              )
  
      for (
          modified_layer_key_names,
          original_layer_key_names,
      ) in self.original_layers_mapping.items():
          if isinstance(original_layer_key_names, list):
              current_weight = getattr(self, modified_layer_key_names)
  
              # Split the current weight n chunks - this is useful to split
              # the qkv layers into q, k, v layers for example.
              split_index = current_weight.shape[0] // len(
                  original_layer_key_names
              )
              for i, subparam_name in enumerate(
                  original_layer_key_names
              ):
                  if recurse_getattr(module, subparam_name) is None:
                      # this is for example the case if bias=False is set for a nn.Linear layer
                      continue
  
                  if module not in self.keys_to_ignore:
                      # TODO: remove the clone once https://github.com/huggingface/transformers/pull/27314 & https://github.com/huggingface/safetensors/pull/379 are released.
                      # Safetensors is bugged when using views of tensors.
                      parameter = current_weight[
                          i * split_index : (i + 1) * split_index
                      ].clone()
                      if isinstance(
                          recurse_getattr(module, subparam_name),
                          torch.nn.Parameter,
                      ):
                          parameter = torch.nn.Parameter(parameter)
                      recurse_setattr(module, subparam_name, parameter)
          elif isinstance(original_layer_key_names, str):
              if (
                  recurse_getattr(module, original_layer_key_names)
                  is None
              ):
                  # this is for example the case if bias=False is set for a nn.Linear layer
                  continue
  
              parameter = getattr(self, modified_layer_key_names)
              if isinstance(
                  recurse_getattr(module, original_layer_key_names),
                  torch.nn.Parameter,
              ):
                  parameter = torch.nn.Parameter(parameter)
              recurse_setattr(
                  module, original_layer_key_names, parameter
              )
          else:
              raise ValueError(
                  f"Invalid type {type(modified_layer_key_names)} for `original_layers_mapping`",
                  " please use either `str` or `list`.",
              )
      return module
  
imports: from typing import TYPE_CHECKING
  import torch
  from ...utils import logging, recurse_getattr, recurse_setattr
label_desc: Use image node_id 3 for calling the BetterTransformerBaseLayer obj's underlying member method code with example usage: obj._revert(module) and returns: module, module