class_docstr: A linear mapping with a given mask on the weights (arbitrary bias)
  
  :param in_features: the number of input features
  :type in_features: int
  :param out_features: the number of output features
  :type out_features: int
  :param mask: the mask to apply to the in_features x out_features weight matrix
  :type mask: torch.Tensor
  :param bias: whether or not `MaskedLinear` should include a bias term. defaults to `True`
  :type bias: bool
class_docstr_tok: ["A","linear","mapping","with","a","given","mask","on","the","weights","(","arbitrary","bias",")",":","param","in_features",":","the","number","of","input","features",":","type","in_features",":","int",":","param","out_features",":","the","number","of","output","features",":","type","out_features",":","int",":","param","mask",":","the","mask","to","apply","to","the","in_features","x","out_features","weight","matrix",":","type","mask",":","torch.Tensor",":","param","bias",":","whether","or","not","`","MaskedLinear","`","should","include","a","bias","term",".","defaults","to","`","True","`",":","type","bias",":","bool"]
code: def forward(self, _input):
      masked_weight = self.weight * self.mask
      return F.linear(_input, masked_weight, self.bias)
  
imports: import warnings
  import torch
  import torch.nn
  from torch.nn import functional
label_desc: Use image node_id 2 for calling the MaskedLinear obj's underlying member method code with example usage: obj.forward(_input) and returns: F