code: def __call__(self, *args, **kwargs):
      if (
          kwargs["past_key_values"] is None
          and self._default.config.use_cache
      ):
          kwargs["past_key_values"] = generate_past_key_values(
              self._default, kwargs["input_ids"].shape[0], 0
          )
      kwargs.pop("position_ids", None)
      for k in list(kwargs.keys()):
          if kwargs[k] is None or isinstance(kwargs[k], bool):
              kwargs.pop(k)
      outputs = self._optimized(**kwargs)
      lm_logits = outputs[0]
      past_key_values = outputs[1]
      fixed_output = CausalLMOutputWithPast(
          loss=None,
          logits=lm_logits,
          past_key_values=past_key_values,
          hidden_states=None,
          attentions=None,
      )
      return fixed_output
  
imports: import argparse
  import inspect
  import logging
  from typing import Tuple
  import torch
  from accelerate import PartialState
  from accelerate.utils import set_seed
  from transformers import AutoTokenizer, BloomForCausalLM, BloomTokenizerFast, CTRLLMHeadModel, CTRLTokenizer, GenerationMixin, GPT2LMHeadModel, GPT2Tokenizer, GPTJForCausalLM, LlamaForCausalLM, LlamaTokenizer, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer, OPTForCausalLM, TransfoXLLMHeadModel, TransfoXLTokenizer, XLMTokenizer, XLMWithLMHeadModel, XLNetLMHeadModel, XLNetTokenizer
  from transformers.modeling_outputs import CausalLMOutputWithPast
label_desc: Use image node_id 2 for calling the _ModelFallbackWrapper obj's underlying member method code with example usage: obj.__call__() and returns: fixed_output