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"]
docstr: Returns definitions of module input types
          
docstr_tok: ["Returns","definitions","of","module","input","types"]
code: def input_types(self):
      """Returns definitions of module input types"""
      return {
          "input_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 1 for calling the SpecCutout obj's underlying member method code with example usage: obj.input_types() and returns: dict