class_docstr: A conditional stochastic model represents a model used for causal mechanisms for non-root nodes in a graphical
  causal model.
class_docstr_tok: ["A","conditional","stochastic","model","represents","a","model","used","for","causal","mechanisms","for","non-root","nodes","in","a","graphical","causal","model","."]
docstr: Draws samples for the fitted model.
docstr_tok: ["Draws","samples","for","the","fitted","model","."]
code: def draw_samples(self, parent_samples: np.ndarray) -> np.ndarray:
      """Draws samples for the fitted model."""
      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 2 for calling the ConditionalStochasticModel obj's underlying member method code with example usage: obj.draw_samples(parent_samples) without return types