docstr: Set parameters for the explainer.
docstr_tok: ["Set","parameters","for","the","explainer","."]
code: def set_params(self, *argv, **kwargs):
      """
      Set parameters for the explainer.
      """
      pass
  
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_Explainer obj's underlying member method code with example usage: obj.set_params() without return types