class_docstr: Distort signal by clipping a random percentage of points
  
  The percentage of points that will be clipped is drawn from a uniform distribution between
  the two input parameters min_percentile_threshold and max_percentile_threshold. If for instance
  30% is drawn, the samples are clipped if they're below the 15th or above the 85th percentile.
code: def apply(self, samples: NDArray[np.float32], sample_rate: int):
      lower_percentile_threshold = int(
          self.parameters["percentile_threshold"] / 2
      )
      lower_threshold, upper_threshold = np.percentile(
          samples,
          [
              lower_percentile_threshold,
              100 - lower_percentile_threshold,
          ],
      )
      samples = np.clip(samples, lower_threshold, upper_threshold)
      return samples
  
file_path: audiomentations/audiomentations/augmentations/clipping_distortion.py
imports: import random
  import numpy
  from numpy.typing import NDArray
  from audiomentations.core.transforms_interface import BaseWaveformTransform
label_desc: Use image node_id 3 for calling the ClippingDistortion obj's underlying member method code with example usage: obj.apply(samples, sample_rate) and returns: samples