code: def speech_commands_collate(batch):
      tensors, targets, t_labels = [], [], []
      for waveform, label, rate, sid, uid, t_label in batch:
          tensors += [waveform]
          targets += [torch.tensor(label)]
          t_labels += [torch.tensor(t_label)]
      tensors = [item.t() for item in tensors]
      tensors_padded = torch.nn.utils.rnn.pad_sequence(
          tensors, batch_first=True, padding_value=0.0
      )
  
      if (
          len(tensors_padded.size()) == 2
      ):  # no MFCC, add feature dimension
          tensors_padded = tensors_padded.unsqueeze(-1)
      targets = torch.stack(targets)
      t_labels = torch.stack(t_labels)
      return [tensors_padded, targets, t_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 1 for calling a global function with example usage: speech_commands_collate(batch) and returns: list