code: def clipping(
      waveform,
      sample_rate: int,
      min_quantile: float = 0.0,
      max_quantile: float = 0.9,
  ):
      """Apply the clipping distortion to the input signal.
  
      Args:
          waveform (torch.Tensor): audio signal (..., time)
          sample_rate (int): sampling rate in Hz (not used)
          min_quantile (float): lower bound on the total percent of samples to be clipped
          max_quantile (float): upper bound on the total percent of samples to be clipped
  
      Returns:
          ret (torch.Tensor): clipped signal (..., time)
      """
      q = waveform.new_tensor([min_quantile, max_quantile])
      min_, max_ = torch.quantile(waveform, q, dim=-1, keepdim=True)
      ret = torch.clamp(waveform, min_, max_)
      return ret
  
imports: import math
  import random
  from typing import Dict, List, Optional, Tuple, Union
  import numpy
  import torch
  import torchaudio
label_desc: Use image node_id 14 for calling a global function with example usage: clipping(waveform, sample_rate, min_quantile, max_quantile) and returns: ret