class_docstr: Clip audio by specified values. e.g. set a_min=-1.0 and a_max=1.0 to ensure that no
  samples in the audio exceed that extent. This can be relevant for avoiding integer
  overflow or underflow (which results in unintended wrap distortion that can sound
  horrible) when exporting to e.g. 16-bit PCM wav.
  
  Another way of ensuring that all values stay between -1.0 and 1.0 is to apply
  PeakNormalization.
  
  This transform is different from ClippingDistortion in that it takes fixed values
  for clipping instead of clipping a random percentile of the samples. Arguably, this
  transform is not very useful for data augmentation. Instead, think of it as a very
  cheap and harsh limiter (for samples that exceed the allotted extent) that can
  sometimes be useful at the end of a data augmentation pipeline.
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code: def apply(self, samples: NDArray[np.float32], sample_rate: int):
      return np.clip(samples, self.a_min, self.a_max)
  
imports: import numpy
  from numpy.typing import NDArray
  from audiomentations.core.transforms_interface import BaseWaveformTransform
label_desc: Use image node_id 2 for calling the Clip obj's underlying member method code with example usage: obj.apply(samples, sample_rate) and returns: np