class_docstr: A stochastic model represents a model used for causal mechanisms for root nodes in a graphical causal model.
class_docstr_tok: ["A","stochastic","model","represents","a","model","used","for","causal","mechanisms","for","root","nodes","in","a","graphical","causal","model","."]
docstr: Fits the model according to the data.
docstr_tok: ["Fits","the","model","according","to","the","data","."]
code: def fit(self, X: np.ndarray) -> None:
      """Fits the model according to the data."""
      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 1 for calling the StochasticModel obj's underlying member method code with example usage: obj.fit(X) without return types