code: def attribution(
      self,
      model_wrapped,
      prompt,
      completion,
      prompt_fn=None,
      intermediate_log_fn=None,
      *args,
      **kwargs,
  ):
      prompt_fn = (lambda x: x) if prompt_fn is None else prompt_fn
      full_prompt = prompt_fn(prompt) + completion
      completion_len = len(
          model_wrapped.tokenizer.encode(completion)[2:]
      )
      prompt_tokens = model_wrapped.tokenizer.encode(prompt_fn(prompt))
      parts = [
          model_wrapped.tokenizer.decode([t]) for t in prompt_tokens
      ]
      with torch.no_grad():
          inputs = model_wrapped.tokenizer(
              full_prompt, return_tensors="pt"
          ).to(model_wrapped.device)
          activations = model_wrapped.model(
              input_ids=inputs.input_ids,
              attention_mask=inputs.attention_mask,
              output_attentions=True,
          )
          atts = activations["attentions"]
          atts = [att.float().cpu().numpy() for att in atts]
          atts = [self.agg_fn(att, axis=1) for att in atts]
  
      importance_cache = {}
      parts = parts[1:]
      for i, att in enumerate(atts):
          att = att[:, -completion_len:, :-completion_len]
          att = self.agg_fn(att, axis=-2).flatten()
          att = att[1:]
          attribution_scores = [
              (p, a) for p, a in zip(range(len(parts)), att)
          ]
          if intermediate_log_fn is not None:
              intermediate_log_fn([parts, None, attribution_scores], i)
          importance_cache[i] = [parts, None, attribution_scores]
      return importance_cache
  
imports: import torch
  import numpy
  from abc import ABC, abstractmethod
  from sklearn.linear_model import Ridge, lars_path
  from .text_utils import *
  from .perturbation_tools import RemovalPerturbation
label_desc: Use image node_id 2 for calling the AttentionExplanationAllLayers obj's underlying member method code with example usage: obj.attribution(model_wrapped, prompt, completion, prompt_fn, intermediate_log_fn) and returns: importance_cache