class_docstr: Augmenter that mask spectrogram based on frequency by random values.
  
  :param tuple zone: Default value is (0.2, 0.8). Assign a zone for augmentation. By default, no any augmentation
       will be applied in first 20% and last 20% of whole audio.
  :param float coverage: Default value is 1 and value should be between 0 and 1. Portion of augmentation. 
      If `1` is assigned, augment operation will be applied to target audio segment. For example, the audio 
      duration is 60 seconds while zone and coverage are (0.2, 0.8) and 0.7 respectively. 42 
      seconds ((0.8-0.2)*0.7*60) audio will be chosen for augmentation.
  :param str name: Name of this augmenter
  
  >>> import nlpaug.augmenter.spectogram as nas
  >>> aug = nas.TimeMaskingAug()
docstr: From: https://arxiv.org/pdf/1904.08779.pdf,
  Time masking is applied so that t consecutive time steps
  [t0, t0 + t) are masked, where t is first chosen from a
  uniform distribution from 0 to the time mask parameter
  T, and t0 is chosen from [0, tau - t).
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code: def substitute(self, data):
      """
      From: https://arxiv.org/pdf/1904.08779.pdf,
      Time masking is applied so that t consecutive time steps
      [t0, t0 + t) are masked, where t is first chosen from a
      uniform distribution from 0 to the time mask parameter
      T, and t0 is chosen from [0, tau - t).
      """
  
      tau = data.shape[1]
      t0, time_end = self.get_augment_range_by_coverage(data)
      t = self.get_random_factor(high=time_end, dtype="int")
  
      if not self.stateless:
          self.tau, self.t, self.t0 = tau, t, t0
  
      return self.model.manipulate(data, t=t, t0=t0)
  
file_path: nlpaug/nlpaug/augmenter/spectrogram/time_masking.py
imports: import numpy
  from nlpaug.augmenter.spectrogram import SpectrogramAugmenter
  from nlpaug.util import Action
  import nlpaug.model.spectrogram
label_desc: Use image node_id 2 for calling the TimeMaskingAug obj's underlying member method code with example usage: obj.substitute(data) and returns: self