docstr: Create a prompt from the mel codes. This is used to condition the model on the mel codes.
  Pad the prompt with start and stop mel tokens.
docstr_tok: ["Create","a","prompt","from","the","mel","codes",".","This","is","used","to","condition","the","model","on","the","mel","codes",".","Pad","the","prompt","with","start","and","stop","mel","tokens","."]
code: def get_prompts(self, prompt_codes):
      """
      Create a prompt from the mel codes. This is used to condition the model on the mel codes.
      Pad the prompt with start and stop mel tokens.
      """
      prompt = prompt_codes
      if self.training:
          lengths = []
          # Compute the real prompt length based on the first encounter with the token 83 used for padding
          for i in range(prompt_codes.shape[0]):
              length = 0
              for j in range(prompt_codes.shape[1]):
                  if prompt_codes[i, j] == 83:
                      break
                  else:
                      length += 1
              lengths.append(length)
  
          # prompt_len = random.randint(1, 9)  # in secs
          prompt_len = 3
          prompt_len = prompt_len * 24  # in frames
          if prompt_codes.shape[-1] >= prompt_len:
              for i in range(prompt_codes.shape[0]):
                  if lengths[i] < prompt_len:
                      start = 0
                  else:
                      start = random.randint(0, lengths[i] - prompt_len)
              prompt = prompt_codes[:, start : start + prompt_len]
  
      # add start and stop tokens
      prompt = F.pad(prompt, (1, 0), value=self.start_prompt_token)
      prompt = F.pad(prompt, (0, 1), value=self.stop_prompt_token)
      return prompt
  
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 8 for calling the GPT obj's underlying member method code with example usage: obj.get_prompts(prompt_codes) and returns: prompt