code: def generate_fine(
      x_coarse_gen,
      model,
      history_prompt=None,
      temp=0.5,
      silent=True,
      base=None,
  ):
      """Generate full audio codes from coarse audio codes.
  
      Args:
          x_coarse_gen (np.ndarray): The coarse audio codes to generate full audio codes from.
          model (BarkModel): The BarkModel to use for generating the full audio codes.
          history_prompt (tuple): A tuple of (semantic_history, coarse_history, fine_history) to use as a prompt for the generation.
          temp (float): The temperature to use for the generation.
          silent (bool): Whether to silence the tqdm progress bar.
          base (tuple): A tuple of (semantic_history, coarse_history, fine_history) to use as a base for the generation.
  
      Returns:
          np.ndarray: The generated full audio codes.
      """
      assert (
          isinstance(x_coarse_gen, np.ndarray)
          and len(x_coarse_gen.shape) == 2
          and 1
          <= x_coarse_gen.shape[0]
          <= model.config.N_FINE_CODEBOOKS - 1
          and x_coarse_gen.shape[1] > 0
          and x_coarse_gen.min() >= 0
          and x_coarse_gen.max() <= model.config.CODEBOOK_SIZE - 1
      )
      if all(v is not None for v in history_prompt) or base is not None:
          if history_prompt is not None:
              x_fine_history = history_prompt[2]
          if base is not None:
              x_fine_history = base[2]
          assert (
              isinstance(x_fine_history, np.ndarray)
              and len(x_fine_history.shape) == 2
              and x_fine_history.shape[0]
              == model.config.N_FINE_CODEBOOKS
              and x_fine_history.shape[1] >= 0
              and x_fine_history.min() >= 0
              and x_fine_history.max() <= model.config.CODEBOOK_SIZE - 1
          )
      else:
          x_fine_history = None
      n_coarse = x_coarse_gen.shape[0]
      # make input arr
      in_arr = np.vstack(
          [
              x_coarse_gen,
              np.zeros(
                  (
                      model.config.N_FINE_CODEBOOKS - n_coarse,
                      x_coarse_gen.shape[1],
                  )
              )
              + model.config.CODEBOOK_SIZE,  # padding
          ]
      ).astype(np.int32)
      # prepend history if available (max 512)
      if x_fine_history is not None:
          x_fine_history = x_fine_history.astype(np.int32)
          in_arr = np.hstack(
              [
                  x_fine_history[:, -512:].astype(np.int32),
                  in_arr,
              ]
          )
          n_history = x_fine_history[:, -512:].shape[1]
      else:
          n_history = 0
      n_remove_from_end = 0
      # need to pad if too short (since non-causal model)
      if in_arr.shape[1] < 1024:
          n_remove_from_end = 1024 - in_arr.shape[1]
          in_arr = np.hstack(
              [
                  in_arr,
                  np.zeros(
                      (
                          model.config.N_FINE_CODEBOOKS,
                          n_remove_from_end,
                      ),
                      dtype=np.int32,
                  )
                  + model.config.CODEBOOK_SIZE,
              ]
          )
      # we can be lazy about fractional loop and just keep overwriting codebooks
      n_loops = (
          np.max(
              [
                  0,
                  int(
                      np.ceil(
                          (x_coarse_gen.shape[1] - (1024 - n_history))
                          / 512
                      )
                  ),
              ]
          )
          + 1
      )
      with inference_mode():
          in_arr = torch.tensor(in_arr.T).to(model.device)
          for n in tqdm.tqdm(range(n_loops), disable=silent):
              start_idx = np.min([n * 512, in_arr.shape[0] - 1024])
              start_fill_idx = np.min(
                  [n_history + n * 512, in_arr.shape[0] - 512]
              )
              rel_start_fill_idx = start_fill_idx - start_idx
              in_buffer = in_arr[start_idx : start_idx + 1024, :][None]
              for nn in range(n_coarse, model.config.N_FINE_CODEBOOKS):
                  logits = model.fine_model(nn, in_buffer)
                  if temp is None:
                      relevant_logits = logits[
                          0,
                          rel_start_fill_idx:,
                          : model.config.CODEBOOK_SIZE,
                      ]
                      codebook_preds = torch.argmax(relevant_logits, -1)
                  else:
                      relevant_logits = (
                          logits[0, :, : model.config.CODEBOOK_SIZE]
                          / temp
                      )
                      probs = F.softmax(relevant_logits, dim=-1)
                      codebook_preds = torch.hstack(
                          [
                              torch.multinomial(probs[n], num_samples=1)
                              for n in range(rel_start_fill_idx, 1024)
                          ]
                      )
                  in_buffer[0, rel_start_fill_idx:, nn] = codebook_preds
                  del logits, codebook_preds
              # transfer over info into model_in and convert to numpy
              for nn in range(n_coarse, model.config.N_FINE_CODEBOOKS):
                  in_arr[
                      start_fill_idx : start_fill_idx
                      + (1024 - rel_start_fill_idx),
                      nn,
                  ] = in_buffer[0, rel_start_fill_idx:, nn]
              del in_buffer
          gen_fine_arr = in_arr.detach().cpu().numpy().squeeze().T
          del in_arr
      gen_fine_arr = gen_fine_arr[:, n_history:]
      if n_remove_from_end > 0:
          gen_fine_arr = gen_fine_arr[:, :-n_remove_from_end]
      assert gen_fine_arr.shape[-1] == x_coarse_gen.shape[-1]
      clear_cuda_cache()
      return gen_fine_arr
  
imports: import logging
  import os
  import re
  from glob import glob
  from typing import Dict, List
  import librosa
  import numpy
  import torch
  import torchaudio
  import tqdm
  from encodec.utils import convert_audio
  from scipy.special import softmax
  from torch.nn import functional
  from TTS.tts.layers.bark.hubert.hubert_manager import HubertManager
  from TTS.tts.layers.bark.hubert.kmeans_hubert import CustomHubert
  from TTS.tts.layers.bark.hubert.tokenizer import HubertTokenizer
  from TTS.tts.layers.bark.load_model import clear_cuda_cache, inference_mode
label_desc: Use image node_id 14 for calling a global function with example usage: generate_fine(x_coarse_gen, model, history_prompt, temp, silent, base) and returns: gen_fine_arr