code: def __call__(
      self, samples: NDArray[np.float32], sample_rate: int
  ) -> NDArray[np.float32]:
      if samples.dtype == np.float64:
          warnings.warn(
              "Warning: input samples dtype is np.float64. Converting to np.float32"
          )
          samples = np.float32(samples)
      if (
          not self.are_parameters_frozen
          or self.parameters["should_apply"] is None
      ):
          self.randomize_parameters(samples, sample_rate)
      if self.parameters["should_apply"] and len(samples) > 0:
          if self.is_multichannel(samples):
              # Note: We multiply by 8 here to allow big batches of very short audio
              if samples.shape[0] > samples.shape[1] * 8:
                  raise WrongMultichannelAudioShape(
                      "Multichannel audio must have channels first, not channels"
                      " last. In other words, the shape must be (channels, samples),"
                      " not (samples, channels). See"
                      " https://iver56.github.io/audiomentations/guides/multichannel_audio_array_shapes/"
                      " for more info."
                  )
              if not self.supports_multichannel:
                  raise MultichannelAudioNotSupportedException(
                      "{} only supports mono audio, not multichannel audio. In other"
                      " words, a 1-dimensional input ndarray was expected, but the"
                      " input had more than 1 dimension.".format(
                          self.__class__.__name__
                      )
                  )
          elif not self.supports_mono:
              raise MonoAudioNotSupportedException(
                  "{} only supports multichannel audio, not mono audio".format(
                      self.__class__.__name__
                  )
              )
          return self.apply(samples, sample_rate)
      return samples
  
imports: import random
  import warnings
  import numpy
  from numpy.typing import NDArray
  from audiomentations.core.utils import is_waveform_multichannel, is_spectrogram_multichannel
label_desc: Use image node_id 3 for calling the BaseWaveformTransform obj's underlying member method code with example usage: obj.__call__(samples, sample_rate) and returns: samples, self