class_docstr: Apply speed purturbation to (synthetic) Tensor data
class_docstr_tok: ["Apply","speed","purturbation","to","(","synthetic",")","Tensor","data"]
code: def __init__(
      self, signals: List[Tuple[torch.Tensor, int]], sample_rate: int
  ):
      super().__init__()
      self.signals = signals
      self.sample_rate = sample_rate
      self.rng = None
  
imports: import os
  import platform
  import sys
  from concurrent.futures import ProcessPoolExecutor
  from typing import List, Tuple
  from unittest import skipIf
  import numpy
  import torch
  import torchaudio
  from torchaudio_unittest.common_utils import get_whitenoise, PytorchTestCase, save_wav, skipIfNoSox, TempDirMixin
label_desc: Use image node_id 1 to create a new RandomPerturbationTensor object from inherited base classes: torch.utils.data with example: obj = RandomPerturbationTensor(signals, sample_rate)