class_docstr: Apply padding to the audio signal - take a fraction of the end or the start of the
  audio and replace that part with padding. This can be useful for preparing ML models
  with constant input length for padded inputs.
code: def randomize_parameters(
      self, samples: NDArray[np.float32], sample_rate: int
  ):
      super().randomize_parameters(samples, sample_rate)
      if self.parameters["should_apply"]:
          input_length = samples.shape[-1]
          self.parameters["padding_length"] = random.randint(
              int(round(self.min_fraction * input_length)),
              int(round(self.max_fraction * input_length)),
          )
  
file_path: audiomentations/audiomentations/augmentations/padding.py
imports: import random
  import numpy
  from numpy.typing import NDArray
  from audiomentations.core.transforms_interface import BaseWaveformTransform
label_desc: Use image node_id 2 for calling the Padding obj's underlying member method code with example usage: obj.randomize_parameters(samples, sample_rate) without return types