class_docstr: Mask a set of frequencies in a spectrogram, à la Google AI SpecAugment. This type of data
  augmentation has proved to make speech recognition models more robust.
  
  The masked frequencies can be replaced with either the mean of the original values or a
  given constant (e.g. zero).
code: def apply(self, magnitude_spectrogram):
      if self.fill_mode == "mean":
          fill_value = np.mean(
              magnitude_spectrogram[
                  self.parameters[
                      "start_frequency_index"
                  ] : self.parameters["end_frequency_index"]
              ]
          )
      else:
          # self.fill_mode == "constant"
          fill_value = self.fill_constant
      magnitude_spectrogram = magnitude_spectrogram.copy()
      magnitude_spectrogram[
          self.parameters["start_frequency_index"] : self.parameters[
              "end_frequency_index"
          ]
      ] = fill_value
      return magnitude_spectrogram
  
file_path: audiomentations/audiomentations/spec_augmentations/spec_frequency_mask.py
imports: import random
  import numpy
  from audiomentations.core.transforms_interface import BaseSpectrogramTransform
label_desc: Use image node_id 3 for calling the SpecFrequencyMask obj's underlying member method code with example usage: obj.apply(magnitude_spectrogram) and returns: magnitude_spectrogram