class_docstr: Wrapper class for non linear function or module.
  
  Args:
      non_linear (callable or type): Non-linear function or type to be
          wrapped.
class_docstr_tok: ["Wrapper","class","for","non","linear","function","or","module",".","Args",":","non_linear","(","callable","or","type",")",":","Non-linear","function","or","type","to","be","wrapped","."]
docstr: Forward method.
  
  Args:
      input_value (torch.Tensor): Input values
  
  Returns:
      torch.Tensor: Output value
docstr_tok: ["Forward","method",".","Args",":","input_value","(","torch.Tensor",")",":","Input","values","Returns",":","torch.Tensor",":","Output","value"]
code: def forward(self, input_value):
      """Forward method.
  
      Args:
          input_value (torch.Tensor): Input values
  
      Returns:
          torch.Tensor: Output value
  
      """
      return self.module(input_value)
  
imports: import copy
  import math
  import warnings
  import torch
  from torch import nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the NonLinearity obj's underlying member method code with example usage: obj.forward(input_value) and returns: self