code: def decode(self, input_ids_or_logits):
      if self.tokenizer is not None:
          if isinstance(input_ids_or_logits, list):
              return self.tokenizer.decode(input_ids_or_logits)
          elif len(input_ids_or_logits.shape) > 2:
              input_ids = self.argmax_select_next_token(
                  input_ids_or_logits, None
              )
          else:
              input_ids = input_ids_or_logits
          try:
              return self.tokenizer.decode(input_ids)
          finally:
              return self.tokenizer.decode(input_ids.squeeze(0))
  
imports: import torch
  import json, os, msgpack
  from transformers.utils import logging
  import types
  import inspect
  import copy
  from typing import Optional, List, Callable
  from drugs.inject_mixin import InjectDrugsMixin, MODEL_NAME_MAPPING
  from transformers import GenerationMixin
  from transformers.cache_utils import DynamicCache
  from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList
  from transformers import AutoTokenizer, TextStreamer, GenerationConfig
  import numpy
  import torch
  import torch.nn.functional
  import hashlib
label_desc: Use image node_id 42 for calling the DRUGS obj's underlying member method code with example usage: obj.decode(input_ids_or_logits) and returns: self, self, self