code: def load_dataset(
      self, split: str, task_cfg: AudioFinetuningConfig = None, **kwargs
  ):
      super().load_dataset(split, task_cfg, **kwargs)
  
      task_cfg = task_cfg or self.cfg
      assert task_cfg.labels is not None
      text_compression_level = getattr(
          TextCompressionLevel, str(self.cfg.text_compression_level)
      )
      data_path = self.cfg.data
      if task_cfg.multi_corpus_keys is None:
          label_path = os.path.join(
              data_path, f"{split}.{task_cfg.labels}"
          )
          skipped_indices = getattr(
              self.datasets[split], "skipped_indices", set()
          )
          text_compressor = TextCompressor(level=text_compression_level)
          with open(label_path, "r") as f:
              labels = [
                  text_compressor.compress(l)
                  for i, l in enumerate(f)
                  if i not in skipped_indices
              ]
  
          assert len(labels) == len(self.datasets[split]), (
              f"labels length ({len(labels)}) and dataset length "
              f"({len(self.datasets[split])}) do not match"
          )
  
          process_label = LabelEncoder(self.target_dictionary)
  
          self.datasets[split] = AddTargetDataset(
              self.datasets[split],
              labels,
              pad=self.target_dictionary.pad(),
              eos=self.target_dictionary.eos(),
              batch_targets=True,
              process_label=process_label,
              label_len_fn=label_len_fn,
              add_to_input=task_cfg.get("autoregressive", False),
              text_compression_level=text_compression_level,
          )
      else:
          target_dataset_map = OrderedDict()
  
          multi_corpus_keys = [
              k.strip() for k in task_cfg.multi_corpus_keys.split(",")
          ]
          corpus_idx_map = {
              k: idx for idx, k in enumerate(multi_corpus_keys)
          }
  
          data_keys = [k.split(":") for k in split.split(",")]
  
          multi_corpus_sampling_weights = [
              float(val.strip())
              for val in task_cfg.multi_corpus_sampling_weights.split(
                  ","
              )
          ]
          data_weights = []
          for key, file_name in data_keys:
              k = key.strip()
              label_path = os.path.join(
                  data_path, f"{file_name.strip()}.{task_cfg.labels}"
              )
              skipped_indices = getattr(
                  self.dataset_map[split][k], "skipped_indices", set()
              )
              text_compressor = TextCompressor(
                  level=text_compression_level
              )
              with open(label_path, "r") as f:
                  labels = [
                      text_compressor.compress(l)
                      for i, l in enumerate(f)
                      if i not in skipped_indices
                  ]
  
              assert len(labels) == len(self.dataset_map[split][k]), (
                  f"labels length ({len(labels)}) and dataset length "
                  f"({len(self.dataset_map[split][k])}) do not match"
              )
  
              process_label = LabelEncoder(self.target_dictionary)
  
              # TODO: Remove duplication of code from the if block above
              target_dataset_map[k] = AddTargetDataset(
                  self.dataset_map[split][k],
                  labels,
                  pad=self.target_dictionary.pad(),
                  eos=self.target_dictionary.eos(),
                  batch_targets=True,
                  process_label=process_label,
                  label_len_fn=label_len_fn,
                  add_to_input=task_cfg.get("autoregressive", False),
                  text_compression_level=text_compression_level,
              )
  
              data_weights.append(
                  multi_corpus_sampling_weights[corpus_idx_map[k]]
              )
  
          if len(target_dataset_map) == 1:
              self.datasets[split] = list(target_dataset_map.values())[
                  0
              ]
          else:
              self.datasets[split] = MultiCorpusDataset(
                  target_dataset_map,
                  distribution=data_weights,
                  seed=0,
                  sort_indices=True,
              )
  
imports: import logging
  import os
  from fairseq.data.multi_corpus_dataset import MultiCorpusDataset
  import torch
  import json
  from argparse import Namespace
  from dataclasses import dataclass, field
  from typing import Optional, Any, OrderedDict
  from fairseq.data import AddTargetDataset, Dictionary, encoders
  from fairseq.tasks.audio_pretraining import AudioPretrainingTask, AudioPretrainingConfig
  from fairseq.dataclass import FairseqDataclass
  from fairseq.dataclass.configs import GenerationConfig
  from fairseq.data.text_compressor import TextCompressor, TextCompressionLevel
  from .None import register_task
  from ..None import utils
  from ..logging import metrics
label_desc: Use image node_id 3 for calling the AudioFinetuningTask obj's underlying member method code with example usage: obj.load_dataset(split, task_cfg) without return types