class_docstr: Represents an post nonlinear FCM, i.e. models of the form:
      Y = g(f(X) + N),
  where X are parent nodes of the target node Y, f an arbitrary prediction model expecting inputs from the
  parents X, N a noise variable and g an invertible function.
class_docstr_tok: ["Represents","an","post","nonlinear","FCM",",","i.e",".","models","of","the","form",":","Y","=","g","(","f","(","X",")","+","N",")",",","where","X","are","parent","nodes","of","the","target","node","Y",",","f","an","arbitrary","prediction","model","expecting","inputs","from","the","parents","X",",","N","a","noise","variable","and","g","an","invertible","function","."]
code: def invertible_function(self) -> InvertibleFunction:
      return self._invertible_function
  
imports: import copy
  from abc import ABC, abstractmethod
  from typing import List, Optional
  import numpy
  from dowhy.gcm.ml import ClassificationModel, PredictionModel
  from dowhy.gcm.ml.regression import InvertibleFunction, SklearnRegressionModel
  from dowhy.gcm.util.general import is_categorical, is_discrete, shape_into_2d
label_desc: Use image node_id 10 for calling the PostNonlinearModel obj's underlying member method code with example usage: obj.invertible_function() and returns: self