class_docstr: Apply a user-defined transform (callable) to the signal.
docstr: :param transform: A callable to be applied over samples. It should input
      samples (ndarray), sample_rate (int) and optionally some user-defined
      keyword arguments.
  :param p: The probability of applying this transform
  :param **kwargs: Any extra keyword arguments to be passed to the transform.
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code: def __init__(self, transform: Callable, p: float = 0.5, **kwargs):
      """
      :param transform: A callable to be applied over samples. It should input
          samples (ndarray), sample_rate (int) and optionally some user-defined
          keyword arguments.
      :param p: The probability of applying this transform
      :param **kwargs: Any extra keyword arguments to be passed to the transform.
      """
      super().__init__(p=p)
      self.transform = transform
      self.kwargs = kwargs
  
file_path: audiomentations/audiomentations/augmentations/lambda_transform.py
imports: from typing import Callable
  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 Lambda object from inherited base classes: BaseWaveformTransform with example: obj = Lambda(transform, p)