class_docstr: Reimplemmentation of WeightDrop for Conv2D. Thanks to PytorchNLP for the initial implementation
  of class WeightDropLinear. Their `License
  <https://github.com/PetrochukM/PyTorch-NLP/blob/master/LICENSE>`__.
  Wrapper around :class: 'torch.nn.Conv' that adds '' weight_dropout '' named argument.
  
  Args:
      weight_dropout (float): The probability a weight will be dropped.
class_docstr_tok: ["Reimplemmentation","of","WeightDrop","for","Conv2D",".","Thanks","to","PytorchNLP","for","the","initial","implementation","of","class","WeightDropLinear",".","Their","`","License","<","https",":","\/\/github.com\/PetrochukM\/PyTorch-NLP\/blob\/master\/LICENSE",">","`","__",".","Wrapper","around",":","class",":","'torch.nn.Conv","'","that","adds","``","weight_dropout","``","named","argument",".","Args",":","weight_dropout","(","float",")",":","The","probability","a","weight","will","be","dropped","."]
code: def forward(self, input):
      kwargs = {
          "input": input,
          "weight": torch.nn.functional.dropout(
              self.weight, p=self._weight_dropout, training=True
          ),
      }
      if self._torch_version >= parse_version("1.8.0"):
          # Bias was added as a required argument in this version.
          kwargs["bias"] = self.bias
      return self._conv_forward(**kwargs)
  
imports: import copy
  import warnings
  from typing import List, Optional, cast, Dict
  from baal.bayesian.common import replace_layers_in_module, _patching_wrapper, BayesianModule
  import torch
  from torch import nn
  from packaging.version import parse
label_desc: Use image node_id 2 for calling the WeightDropConv2d obj's underlying member method code with example usage: obj.forward(input) and returns: self