class_docstr: Base class for a sampler from the interventional distribution.
class_docstr_tok: ["Base","class","for","a","sampler","from","the","interventional","distribution","."]
docstr: OVerride this if your sampling method only allows sampling a point at a time.
  :param : numpy.array: x_z is a numpy array containing the values of x and z in the order of the list given by
  self._treatment_names + self._target_estimand.get_backdoor_variables()
  :return: numpy.array:  a sampled outcome point
docstr_tok: ["OVerride","this","if","your","sampling","method","only","allows","sampling","a","point","at","a","time",".",":","param",":","numpy.array",":","x_z","is","a","numpy","array","containing","the","values","of","x","and","z","in","the","order","of","the","list","given","by","self._treatment_names","+","self._target_estimand.get_backdoor_variables","(",")",":","return",":","numpy.array",":","a","sampled","outcome","point"]
code: def _sample_point(self, x_z):
      """
      OVerride this if your sampling method only allows sampling a point at a time.
      :param : numpy.array: x_z is a numpy array containing the values of x and z in the order of the list given by
      self._treatment_names + self._target_estimand.get_backdoor_variables()
      :return: numpy.array:  a sampled outcome point
      """
      raise NotImplementedError
  
file_path: dowhy/dowhy/do_sampler.py
imports: import logging
  from typing import List
  import networkx
  import numpy
  import pandas
  from dowhy import EstimandType, identify_effect_auto
  from dowhy.utils.api import parse_state
label_desc: Use image node_id 2 for calling the DoSampler obj's underlying member method code with example usage: obj._sample_point(x_z) without return types