code: def post_init_gpt2_config(self, kv_cache=True):
      seq_length = self.max_mel_tokens + self.max_text_tokens + 2
      gpt_config = GPT2Config(
          vocab_size=self.max_mel_tokens,
          n_positions=seq_length,
          n_ctx=seq_length,
          n_embd=self.model_dim,
          n_layer=self.layers,
          n_head=self.heads,
          gradient_checkpointing=False,
          use_cache=True,
      )
      self.inference_model = GPT2InferenceModel(
          gpt_config,
          self.gpt,
          self.mel_pos_embedding,
          self.mel_embedding,
          self.final_norm,
          self.mel_head,
          kv_cache=kv_cache,
      )
      # self.inference_model = PrunedGPT2InferenceModel(gpt_config, self.gpt, self.mel_pos_embedding, self.mel_embedding, self.final_norm, self.mel_head)
      self.gpt.wte = self.mel_embedding
  
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 2 for calling the UnifiedVoice obj's underlying member method code with example usage: obj.post_init_gpt2_config(kv_cache) without return types