class_docstr: Thanks to PytorchNLP for the initial implementation
  # code from https://pytorchnlp.readthedocs.io/en/latest/_modules/torchnlp/nn/weight_drop.html
  of class WeightDropLinear. Their `License
  <https://github.com/PetrochukM/PyTorch-NLP/blob/master/LICENSE>`__.
  Wrapper around :class:`torch.nn.Linear` that adds ``weight_dropout`` named argument.
  
  Args:
      weight_dropout (float): The probability a weight will be dropped.
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code: def __init__(self, weight_dropout=0.0, **kwargs):
      wanted = ["in_features", "out_features"]
      self._kwargs = {k: v for k, v in kwargs.items() if k in wanted}
      super().__init__(**self._kwargs)
      self._weight_dropout = weight_dropout
  
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 WeightDropLinear object from inherited base classes: torch.nn, WeightDropMixin with example: obj = WeightDropLinear(weight_dropout)