class_docstr: Feature-level dropout: takes an input of size len x num_features and drops
  each feature with probabibility p. A feature is dropped across the full
  portion of the input that corresponds to a single batch element.
class_docstr_tok: ["Feature-level","dropout",":","takes","an","input","of","size","len","x","num_features","and","drops","each","feature","with","probabibility","p.","A","feature","is","dropped","across","the","full","portion","of","the","input","that","corresponds","to","a","single","batch","element","."]
code: def forward(self, input, batch_idxs):
      return FeatureDropoutFunction.apply(
          input, batch_idxs, self.p, self.training, self.inplace
      )
  
imports: import numpy
  import functools
  import sys
  import torch
  from torch.autograd import Variable
  import torch.nn
  import torch.nn.init
label_desc: Use image node_id 2 for calling the FeatureDropout obj's underlying member method code with example usage: obj.forward(input, batch_idxs) and returns: FeatureDropoutFunction