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.
docstr: :param mode: Padding mode. Must be one of "silence", "wrap", "reflect"
  :param min_fraction: Minimum fraction of the signal duration to be padded
  :param max_fraction: Maximum fraction of the signal duration to be padded
  :param pad_section: Which part of the signal should be replaced with padding:
      "start" or "end"
  :param p: The probability of applying this transform
code: def __init__(
      self,
      mode: str = "silence",
      min_fraction: float = 0.01,
      max_fraction: float = 0.7,
      pad_section: str = "end",
      p: float = 0.5,
  ):
      """
      :param mode: Padding mode. Must be one of "silence", "wrap", "reflect"
      :param min_fraction: Minimum fraction of the signal duration to be padded
      :param max_fraction: Maximum fraction of the signal duration to be padded
      :param pad_section: Which part of the signal should be replaced with padding:
          "start" or "end"
      :param p: The probability of applying this transform
      """
      super().__init__(p)
  
      assert mode in ("silence", "wrap", "reflect")
      self.mode = mode
  
      assert max_fraction <= 1.0
      assert min_fraction >= 0
      assert min_fraction <= max_fraction
      self.min_fraction = min_fraction
      self.max_fraction = max_fraction
  
      assert pad_section in ("start", "end")
      self.pad_section = pad_section
  
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 1 to create a new Padding object from inherited base classes: BaseWaveformTransform with example: obj = Padding(mode, min_fraction, max_fraction, pad_section, p)