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 __init__(self, p=0.5, inplace=False):
      super().__init__()
      if p < 0 or p > 1:
          raise ValueError(
              "dropout probability has to be between 0 and 1, "
              "but got {}".format(p)
          )
      self.p = p
      self.inplace = 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 1 to create a new FeatureDropout object from inherited base classes: nn with example: obj = FeatureDropout(p, inplace)