class_docstr: Represents a Functional Causal Model (FCM), a specific type of conditional stochastic model, that is defined
  as:
      Y := f(X, N), N: Noise
class_docstr_tok: ["Represents","a","Functional","Causal","Model","(","FCM",")",",","a","specific","type","of","conditional","stochastic","model",",","that","is","defined","as",":","Y",":","=","f","(","X",",","N",")",",","N",":","Noise"]
code: def evaluate(
      self, parent_samples: np.ndarray, noise_samples: np.ndarray
  ) -> np.ndarray:
      raise NotImplementedError
  
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 3 for calling the FunctionalCausalModel obj's underlying member method code with example usage: obj.evaluate(parent_samples, noise_samples) without return types