class_docstr: Google's Speech Commands dataset for keyword spotting (https://arxiv.org/abs/1804.03209).
  
  This contains the "v2" version for 12-way classification (10 commands,
  plus unknown and background categories).
  
  Args:
      opts: Command-line arguments
docstr: Get the dataset sample at the given index.
docstr_tok: ["Get","the","dataset","sample","at","the","given","index","."]
code: def get_sample(self, index: int) -> Tuple[Tensor, float, Tensor]:
      """
      Get the dataset sample at the given index.
      """
      dataset_entry = self.dataset_entries[index]
      waveform, audio_fps = torchaudio.load(
          dataset_entry["audio_filepath"]
      )
  
      label = torch.tensor(
          self.label_to_index[dataset_entry["command"]],
          dtype=torch.long,
      )
      return waveform, audio_fps, label
  
imports: import argparse
  import json
  import os
  import random
  from typing import Dict, Tuple, Union
  import torch
  import torchaudio
  from torch import Tensor
  from torch.nn import functional
  from data.datasets import DATASET_REGISTRY, dataset_base
  from data.transforms.audio import Noise, Roll, SetFixedLength
  from data.transforms.common import Compose
  from data.transforms.image_pil import BaseTransformation
label_desc: Use image node_id 6 for calling the SpeechCommandsv2Dataset obj's underlying member method code with example usage: obj.get_sample(index) and returns: waveform,  audio_fps,  label