code: def compute_embeddings(
      self,
      cond_latents,
      text_inputs,
  ):
      text_inputs = F.pad(
          text_inputs, (0, 1), value=self.stop_text_token
      )
      text_inputs = F.pad(
          text_inputs, (1, 0), value=self.start_text_token
      )
      emb = self.text_embedding(text_inputs) + self.text_pos_embedding(
          text_inputs
      )
      emb = torch.cat([cond_latents, emb], dim=1)
      self.gpt_inference.store_prefix_emb(emb)
      gpt_inputs = torch.full(
          (
              emb.shape[0],
              emb.shape[1] + 1,  # +1 for the start_audio_token
          ),
          fill_value=1,
          dtype=torch.long,
          device=text_inputs.device,
      )
      gpt_inputs[:, -1] = self.start_audio_token
      return gpt_inputs
  
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 12 for calling the GPT obj's underlying member method code with example usage: obj.compute_embeddings(cond_latents, text_inputs) and returns: gpt_inputs