class_docstr: Zeroes out(cuts) random rectangles in the spectrogram
  as described in (https://arxiv.org/abs/1708.04552).
  
  params:
  rect_masks - how many rectangular masks should be cut
  rect_freq - maximum size of cut rectangles along the frequency dimension
  rect_time - maximum size of cut rectangles along the time dimension
class_docstr_tok: ["Zeroes","out","(","cuts",")","random","rectangles","in","the","spectrogram","as","described","in","(","https",":","\/\/arxiv.org\/abs\/1708.04552",")",".","params",":","rect_masks","-","how","many","rectangular","masks","should","be","cut","rect_freq","-","maximum","size","of","cut","rectangles","along","the","frequency","dimension","rect_time","-","maximum","size","of","cut","rectangles","along","the","time","dimension"]
code: def __init__(self, rect_masks=0, rect_time=5, rect_freq=20, rng=None):
      super(SpecCutout, self).__init__()
  
      self._rng = random.Random() if rng is None else rng
  
      self.rect_masks = rect_masks
      self.rect_time = rect_time
      self.rect_freq = rect_freq
  
imports: import random
  import numpy
  import torch
  import torch.nn
  from nemo.core.classes import Typing, typecheck
  from nemo.core.neural_types import LengthsType, NeuralType, SpectrogramType
label_desc: Use image node_id 3 to create a new SpecCutout object from inherited base classes: nn, Typing with example: obj = SpecCutout(rect_masks, rect_time, rect_freq, rng)