code: def do_some(self, model, streamer=None, tokenizer=None):
      self.model = model
      model_type = model.config.model_type
      if streamer is not None:
          self.streamer = streamer
          self.tokenizer = (
              tokenizer if tokenizer is not None else streamer.tokenizer
          )
      if tokenizer is not None:
          self.tokenizer = tokenizer
      if model_type not in MODEL_NAME_MAPPING:
          raise NotImplementedError(
              f"`drugs` does not support models with the `{model_type}` architecture at this time."
          )
      self.model.dgenerator = self
      self.model.Dgenerate = self.generate
      self.model.cold_shower = self.cold_shower
      layerct = len(model.model.layers)
      # for idx, layer in enumerate(model.model.layers):
      #    layer.total_layers = layerct
      #    layer.layer_idx = idx
      self._update_layer_doses()
      logger.warn(
          f"[Drugs] Injected drugs into {layerct} attention class{'es' if layerct != 1 else ''}."
      )
  
      InjectDrugsMixin._inject_drugged_attention(model)
      # model._update_model_kwargs_for_generation = types.MethodType(_update_model_kwargs_for_generation, model)
  
      return self.model
  
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 48 for calling the DRUGS obj's underlying member method code with example usage: obj.do_some(model, streamer, tokenizer) and returns: self