code: def init_gpt_for_inference(self, kv_cache=True, use_deepspeed=False):
      seq_length = (
          self.max_prompt_tokens
          + self.max_mel_tokens
          + self.max_text_tokens
          + 1
      )
      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.gpt_inference = GPT2InferenceModel(
          gpt_config,
          self.gpt,
          self.mel_pos_embedding,
          self.mel_embedding,
          self.final_norm,
          self.mel_head,
          kv_cache=kv_cache,
      )
      self.gpt.wte = self.mel_embedding
  
      if use_deepspeed:
          import deepspeed
  
          self.ds_engine = deepspeed.init_inference(
              model=self.gpt_inference.half(),  # Transformers models
              mp_size=1,  # Number of GPU
              dtype=torch.float32,  # desired data type of output
              replace_method="auto",  # Lets DS autmatically identify the layer to replace
              replace_with_kernel_inject=True,  # replace the model with the kernel injector
          )
          self.gpt_inference = self.ds_engine.module.eval()
  
imports: import functools
  import math
  import random
  import torch
  import torch.nn
  import torch.nn.functional
  from transformers import GPT2Config
  from TTS.tts.layers.xtts.gpt_inference import GPT2InferenceModel
  from TTS.tts.layers.xtts.latent_encoder import ConditioningEncoder
  from TTS.tts.layers.xtts.perceiver_encoder import PerceiverResampler
label_desc: Use image node_id 3 for calling the GPT obj's underlying member method code with example usage: obj.init_gpt_for_inference(kv_cache, use_deepspeed) without return types