class_docstr: CoFrNet_Model is the base class for Continued Fractions Nets (CoFrNets).
  
  References:
      .. [#1] CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions, NeurIPS 2021. 
      Isha Puri, Amit Dhurandhar, Tejaswini Pedapati, Karthikeyan Shanmugam, Dennis Wei, Kush R Varshney. 
      https://proceedings.neurips.cc/paper/2021/file/b538f279cb2ca36268b23f557a831508-Paper.pdf
class_docstr_tok: ["CoFrNet_Model","is","the","base","class","for","Continued","Fractions","Nets","(","CoFrNets",")",".","References",":","..","[","#","1","]","CoFrNets",":","Interpretable","Neural","Architecture","Inspired","by","Continued","Fractions",",","NeurIPS","2021",".","Isha","Puri",",","Amit","Dhurandhar",",","Tejaswini","Pedapati",",","Karthikeyan","Shanmugam",",","Dennis","Wei",",","Kush","R","Varshney",".","https",":","\/\/proceedings.neurips.cc\/paper\/2021\/file\/b538f279cb2ca36268b23f557a831508-Paper.pdf"]
docstr: Activation function that uses capped 1/x described in paper. Takes in Wx, returns modified activation function of Wx 
docstr_tok: ["Activation","function","that","uses","capped","1\/x","described","in","paper",".","Takes","in","Wx",",","returns","modified","activation","function","of","Wx"]
code: def modified_reciprocal_activation(self, Wx, epsilon=0.1):
      """
      Activation function that uses capped 1/x described in paper. Takes in Wx, returns modified activation function of Wx
      """
      epsilonMatrix = torch.mul(
          torch.full_like(Wx, epsilon), torch.sign(Wx)
      )
      denom = torch.where(torch.abs(Wx) < epsilon, epsilonMatrix, Wx)
      return torch.reciprocal(denom)
  
imports: import sys
  import pandas
  import numpy
  import torch
  import torch.nn
  from torch.autograd import Function
  from torch.nn.parameter import Parameter
  import torch.nn.functional
  from sklearn.preprocessing import MinMaxScaler
  from sklearn.model_selection import train_test_split
  from aix360.algorithms.cofrnet.utils import generate_connections
  from aix360.algorithms.cofrnet.utils import process_data
  from aix360.algorithms.cofrnet.CustomizedLinearClasses import CustomizedLinearFunction
  from aix360.algorithms.cofrnet.CustomizedLinearClasses import CustomizedLinear
  from aix360.algorithms.die import DIExplainer
label_desc: Use image node_id 2 for calling the CoFrNet_Model obj's underlying member method code with example usage: obj.modified_reciprocal_activation(Wx, epsilon) and returns: torch