class_docstr: Base class for a sampler from the interventional distribution.
class_docstr_tok: ["Base","class","for","a","sampler","from","the","interventional","distribution","."]
code: def point_sample(self):
      if self.num_cores == 1:
          sampled_outcomes = self._df[
              self._treatment_names
              + self._target_estimand.get_backdoor_variables()
          ].apply(self._sample_point, axis=1)
      else:
          from multiprocessing import Pool
  
          p = Pool(self.num_cores)
          sampled_outcomes = np.array(
              p.map(
                  self.sampler.sample_point,
                  self._df[
                      self._treatment_names
                      + self._target_estimand.get_backdoor_variables()
                  ].values,
              )
          )
          sampled_outcomes = pd.DataFrame(
              sampled_outcomes, columns=self._outcome_names
          )
      self._df[self._outcome_names] = sampled_outcomes
  
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 6 for calling the DoSampler obj's underlying member method code with example usage: obj.point_sample() without return types