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 __init__(self, weight_dropout=0.0, **kwargs):
      wanted = [
          "in_channels",
          "out_channels",
          "kernel_size",
          "dilation",
          "padding",
      ]
      self._kwargs = {k: v for k, v in kwargs.items() if k in wanted}
      super().__init__(**self._kwargs)
      self._weight_dropout = weight_dropout
      self._torch_version = parse_version(torch.__version__)
  
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 1 to create a new WeightDropConv2d object from inherited base classes: torch.nn, WeightDropMixin with example: obj = WeightDropConv2d(weight_dropout)