code: def __init__(
      self,
      h_dose=0,
      a_dose=0,
      q_dose=0,
      k_dose=0,
      v_dose=0,
      h_shape=None,
      a_shape=None,
      q_shape=None,
      k_shape=None,
      v_shape=None,
      tokenizer=None,
  ):
      self.set_H_dose_theta(h_dose)
      self.set_A_dose_theta(a_dose)
      self.set_Q_dose_theta(q_dose)
      self.set_K_dose_theta(k_dose)
      self.set_V_dose_theta(v_dose)
      if h_shape is not None:
          self.set_A_dose_shape(h_shape)
      if a_shape is not None:
          self.set_A_dose_shape(a_shape)
      if q_shape is not None:
          self.set_Q_dose_shape(q_shape)
      if k_shape is not None:
          self.set_K_dose_shape(k_shape)
      if v_shape is not None:
          self.set_V_dose_shape(v_shape)
  
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 1 to create a new DRUGS object  with example: obj = DRUGS(h_dose, a_dose, q_dose, k_dose, v_dose, h_shape, a_shape, q_shape, k_shape, v_shape, tokenizer)