code: def SpeechCommands(
      root=default_dataset_location("speech_commands"),
      url="speech_commands_v0.02",
      download=True,
      subset=None,
      mfcc_preprocessing=None,
  ):
      """
      root: dataset root location
      url: version name of the dataset
      download: automatically download the dataset, if not present
      subset: one of 'training', 'validation', 'testing'
      mfcc_preprocessing: an optional torchaudio.transforms.MFCC instance
          to preprocess each audio. Warning: this may slow down the execution
          since preprocessing is applied on-the-fly each time a sample is
          retrieved from the dataset.
      """
      dataset = SpeechCommandsData(
          root=root,
          download=download,
          subset=subset,
          url=url,
          mfcc_preprocessing=mfcc_preprocessing,
      )
      labels = [datapoint[1] for datapoint in dataset]
      return _make_taskaware_classification_dataset(
          dataset, collate_fn=speech_commands_collate, targets=labels
      )
  
file_path: avalanche/avalanche/benchmarks/datasets/torchaudio_wrapper.py
imports: import os
  from avalanche.benchmarks.utils import _make_taskaware_classification_dataset
  from avalanche.benchmarks.datasets import default_dataset_location
  import torch
label_desc: Use image node_id 2 for calling a global function with example usage: SpeechCommands(root, url, download, subset, mfcc_preprocessing) and returns: _make_taskaware_classification_dataset