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: Returns the sample corresponding to the input sample index and applies
  transforms.
  
  If the class uses mixup, and is in training mode, this will additionally
  apply mixup.
  
  Args:
      batch_indexes_tup: Tuple of the form (crop_size_h, crop_size_w, sample_index).
          The first two parts are not needed, and are ignored by this function.
  
  Returns:
      A sample as a dictionary. It contains:
          {
              "samples":
                  {
                      "audio": A [C, N] tensor, where C is the number of
                          channels, and N is the length.
                  }
              "targets": an integer class label.
              "sample_id": an integer giving the sample index.
              "metadata":
                  {
                      "audio_fps": The sampling rate of the audio.
                  }
          }
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code: def __getitem__(
      self, batch_indexes_tup: Tuple
  ) -> Dict[str, Union[Dict[str, Tensor], Tensor, int]]:
      """
      Returns the sample corresponding to the input sample index and applies
      transforms.
  
      If the class uses mixup, and is in training mode, this will additionally
      apply mixup.
  
      Args:
          batch_indexes_tup: Tuple of the form (crop_size_h, crop_size_w, sample_index).
              The first two parts are not needed, and are ignored by this function.
  
      Returns:
          A sample as a dictionary. It contains:
              {
                  "samples":
                      {
                          "audio": A [C, N] tensor, where C is the number of
                              channels, and N is the length.
                      }
                  "targets": an integer class label.
                  "sample_id": an integer giving the sample index.
                  "metadata":
                      {
                          "audio_fps": The sampling rate of the audio.
                      }
              }
      """
      _, _, index = batch_indexes_tup
      data = self.get_transformed_sample(index)
  
      if self.mixup and self.is_training:
          index = random.randint(0, len(self) - 1)
          data2 = self.get_transformed_sample(index)
  
          if (
              data["metadata"]["audio_fps"]
              != data2["metadata"]["audio_fps"]
          ):
              raise ValueError(
                  f"Inconsistent audio_fps ({data['metadata']['audio_fps']} and {data2['metadata']['audio_fps']})"
              )
  
          coefficient = torch.rand(1)
          data["samples"]["audio"] = data["samples"][
              "audio"
          ] * coefficient + data2["samples"]["audio"] * (
              1.0 - coefficient
          )
  
          def to_onehot(targets: Tensor) -> Tensor:
              return F.one_hot(
                  targets, num_classes=len(self.label_to_index)
              )
  
          data["targets"] = to_onehot(
              data["targets"]
          ) * coefficient + to_onehot(data2["targets"]) * (
              1.0 - coefficient
          )
  
      return data
  
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 7 for calling the SpeechCommandsv2Dataset obj's underlying member method code with example usage: obj.__getitem__(batch_indexes_tup) and returns: data, F