code: def generate(
      self,
      cond_latents,
      text_inputs,
      **hf_generate_kwargs,
  ):
      gpt_inputs = self.compute_embeddings(cond_latents, text_inputs)
      gen = self.gpt_inference.generate(
          gpt_inputs,
          bos_token_id=self.start_audio_token,
          pad_token_id=self.stop_audio_token,
          eos_token_id=self.stop_audio_token,
          max_length=self.max_gen_mel_tokens + gpt_inputs.shape[-1],
          **hf_generate_kwargs,
      )
      if "return_dict_in_generate" in hf_generate_kwargs:
          return gen.sequences[:, gpt_inputs.shape[1] :], gen
      return gen[:, gpt_inputs.shape[1] :]
  
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 13 for calling the GPT obj's underlying member method code with example usage: obj.generate(cond_latents, text_inputs) and returns: gen, gen,  gen