class_docstr: autograd function which masks it's weights by 'mask'.
class_docstr_tok: ["autograd","function","which","masks","it","'s","weights","by","'mask","'","."]
code: def forward(ctx, input, weight, bias=None, mask=None):
      if mask is not None:
          # change weight to 0 where mask == 0
          weight = weight * mask
      weight_t = weight.t()
  
      output = input.mm(weight_t)
      # output = torch.matmul(input, weight_t)
      if bias is not None:
          output += bias.unsqueeze(0).expand_as(output)
      ctx.save_for_backward(input, weight, bias, mask)
      return output
  
file_path: AIX360/aix360/algorithms/cofrnet/CustomizedLinearClasses.py
imports: import pandas
  import numpy
  import pandas
  import torch
  import torch.nn
  import torch.autograd
  from torch.autograd import Function
  from torch.nn.parameter import Parameter
  import torch.nn.functional
  import math
label_desc: Use image node_id 1 for calling the CustomizedLinearFunction obj's underlying member method code with example usage: obj.forward(ctx, input, weight, bias, mask) and returns: output