code: def __init__(
      self,
      config,
      gpt,
      text_pos_emb,
      embeddings,
      norm,
      linear,
      kv_cache,
  ):
      super().__init__(config)
      self.transformer = gpt
      self.text_pos_embedding = text_pos_emb
      self.embeddings = embeddings
      self.lm_head = nn.Sequential(norm, linear)
      self.kv_cache = kv_cache
  
imports: import functools
  import torch
  import torch.nn
  import torch.nn.functional
  from transformers import GPT2Config, GPT2PreTrainedModel, LogitsProcessorList
  from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
  from TTS.tts.layers.tortoise.arch_utils import AttentionBlock, TypicalLogitsWarper
label_desc: Use image node_id 1 to create a new GPT2InferenceModel object from inherited base classes: GPT2PreTrainedModel with example: obj = GPT2InferenceModel(config, gpt, text_pos_emb, embeddings, norm, linear, kv_cache)