code: def forward(self, input):
      # See the autograd section for explanation of what happens here.
      return CustomizedLinearFunction.apply(
          input, self.weight, self.bias, self.mask
      )
  
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 3 for calling the CustomizedLinear obj's underlying member method code with example usage: obj.forward(input) and returns: CustomizedLinearFunction