class_docstr: Zeroes out(cuts) random continuous horisontal or
  vertical segments of the spectrogram as described in
  SpecAugment (https://arxiv.org/abs/1904.08779).
  
  params:
  freq_masks - how many frequency segments should be cut
  time_masks - how many time segments should be cut
  freq_width - maximum number of frequencies to be cut in one segment
  time_width - maximum number of time steps to be cut in one segment.
      Can be a positive integer or a float value in the range [0, 1].
      If positive integer value, defines maximum number of time steps
      to be cut in one segment.
      If a float value, defines maximum percentage of timesteps that
      are cut adaptively.
class_docstr_tok: ["Zeroes","out","(","cuts",")","random","continuous","horisontal","or","vertical","segments","of","the","spectrogram","as","described","in","SpecAugment","(","https",":","\/\/arxiv.org\/abs\/1904.08779",")",".","params",":","freq_masks","-","how","many","frequency","segments","should","be","cut","time_masks","-","how","many","time","segments","should","be","cut","freq_width","-","maximum","number","of","frequencies","to","be","cut","in","one","segment","time_width","-","maximum","number","of","time","steps","to","be","cut","in","one","segment",".","Can","be","a","positive","integer","or","a","float","value","in","the","range","[","0",",","1","]",".","If","positive","integer","value",",","defines","maximum","number","of","time","steps","to","be","cut","in","one","segment",".","If","a","float","value",",","defines","maximum","percentage","of","timesteps","that","are","cut","adaptively","."]
docstr: Returns definitions of module output types
          
docstr_tok: ["Returns","definitions","of","module","output","types"]
code: def output_types(self):
      """Returns definitions of module output types"""
      return {
          "augmented_spec": NeuralType(
              ("B", "D", "T"), SpectrogramType()
          )
      }
  
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 2 for calling the SpecAugment obj's underlying member method code with example usage: obj.output_types() and returns: dict