code: def replace_layer_with_copy(
      feat: BaseFeature, target_layer: torch.nn.Module
  ):
      """Replaces a layer in a feature with a copy of the layer in-place.
  
      This is useful in a tied weights scenario, where a single encoder may be used by multiple features. If we leave
      as-is, Captum complains about the resulting computation graph. The solution is to create an identical
      (deep) copy of the layer fed into Captum: https://github.com/pytorch/captum/issues/794#issuecomment-1093021638
  
      This is safe to do during the explain step because we are essentially running inference, and no model artifacts are
      being saved during the explain step.
  
      TODO(geoffrey): if a user ever wants to train immediately after explain (i.e. w/o loading weights from the disk),
      we might want to implement this as a context so that we can restore the original encoder object at the end.
      Will defer this implementation for now because that scenario seems unlikely.
  
      At a high-level the approach is the following:
      1. Create a deep-copy of the entire encoder object and set it as the feature's encoder object
      2. Replace the tensors in the copied encoder object with the tensors from the original encoder object, except for
           the tensors in the target layer. We want to explain these tensors, so we want to keep them as deep copies.
  
      This approach ensures that at most 2 copies of the encoder object are in memory at any given time.
      """
      with torch.no_grad():
          # Get the original encoder object and a mapping from param names to the params themselves.
          orig_encoder_obj = feat.encoder_obj
          orig_encoder_obj_state_dict = orig_encoder_obj.state_dict()
  
          # Deep copy the original encoder object and set the copy as this feature's encoder object.
          copy_encoder_obj = deepcopy(orig_encoder_obj)
          feat.encoder_obj = copy_encoder_obj
  
          # We have to get the absolute module key in order to do string matching because the target_layer keys are
          # relative to itself. If we were to leave it as-is and attempt to suffix match, we may get duplicates for
          # common layers i.e. "LayerNorm.weight" and "LayerNorm.bias". Getting the absolute module key ensures we
          # use values like "transformer.module.embedding.LayerNorm.weight" instead.
          keys_to_keep_copy = get_absolute_module_key_from_submodule(
              orig_encoder_obj, target_layer
          )
  
          # Get the tensors to keep from the copied encoder object. These are the tensors in the target layer.
          for key, param in copy_encoder_obj.named_parameters():
              if key not in keys_to_keep_copy:
                  param.data = orig_encoder_obj_state_dict[key].data
  
file_path: ludwig/ludwig/explain/util.py
imports: from copy import deepcopy
  import pandas
  import torch
  from ludwig.api import LudwigModel
  from ludwig.constants import COLUMN, INPUT_FEATURES, PREPROCESSING, SPLIT
  from ludwig.data.split import get_splitter
  from ludwig.features.base_feature import BaseFeature
label_desc: Use image node_id 6 for calling a global function with example usage: replace_layer_with_copy(feat, target_layer) without return types