docstr: https://arxiv.org/pdf/1904.08779.pdf, https://arxiv.org/pdf/2001.01401.pdf
  Frequency masking is applied so that f consecutive mel
  frequency channels [f0, f0 + f) are masked, where f is
  first chosen from a uniform distribution from 0 to the
  frequency mask parameter F, and f0 is chosen from
  [0, v - f). v is the number of mel frequency channels.
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code: def manipulate(self, data, f, f0, time_start, time_end):
      """
      https://arxiv.org/pdf/1904.08779.pdf, https://arxiv.org/pdf/2001.01401.pdf
      Frequency masking is applied so that f consecutive mel
      frequency channels [f0, f0 + f) are masked, where f is
      first chosen from a uniform distribution from 0 to the
      frequency mask parameter F, and f0 is chosen from
      [0, v - f). v is the number of mel frequency channels.
      """
  
      aug_data = data.copy()
      aug_data[f0 : f0 + f, time_start:time_end] = 0
      return aug_data
  
imports: import numpy
  from nlpaug.model.spectrogram import Spectrogram
label_desc: Use image node_id 2 for calling the FrequencyMasking obj's underlying member method code with example usage: obj.manipulate(data, f, f0, time_start, time_end) and returns: aug_data