How to use the dowhy.do_sampler.DoSampler function in dowhy

def get_class_object(method_name, *args, **kwargs):
    # from https://www.bnmetrics.com/blog/factory-pattern-in-python3-simple-version
    try:
        module_name = method_name
        class_name = string.capwords(method_name, "_").replace('_', '')
        do_sampler_module = import_module('.' + module_name, package=PACKAGE_NAME)
        do_sampler_class = getattr(do_sampler_module, class_name)
        assert issubclass(do_sampler_class, DoSampler)

    except (AttributeError, AssertionError, ImportError) as e:
        if isinstance(e, ImportError) and e.name != PACKAGE_NAME + '.' + module_name:
            raise e
        raise ImportError('{} is not an existing do sampler.'.format(method_name))
    return do_sampler_class

from dowhy.do_sampler import DoSampler
from dowhy.utils.propensity_score import propensity_of_treatment_score


class WeightingSampler(DoSampler):
    def __init__(self, data,
                 *args, params=None,
                 variable_types=None, num_cores=1, keep_original_treatment=False,
                 causal_model=None, **kwargs):
        """
        g, df, data_types

        """
        super().__init__(data,
                         params=params,
                         variable_types=variable_types, num_cores=num_cores,
                         keep_original_treatment=keep_original_treatment,
                         causal_model=causal_model)

        self.logger.info("Using WeightingSampler for do sampling.")
        self.logger.info("Caution: do samplers assume iid data.")

from dowhy.do_sampler import DoSampler
import numpy as np
import pymc3 as pm
import networkx as nx


class McmcSampler(DoSampler):
    def __init__(self, data, *args, params=None, variable_types=None,
                 num_cores=1, keep_original_treatment=False,
                 causal_model=None,
                 **kwargs):
        """
        g, df, data_types

        """
        super().__init__(data, params=params, variable_types=variable_types, causal_model=causal_model,
                 num_cores=num_cores, keep_original_treatment=keep_original_treatment)

        self.logger.info("Using McmcSampler for do sampling.")
        self.point_sampler = False
        self.sampler = self._construct_sampler()

        self.g = causal_model._graph.get_unconfounded_observed_subgraph()

from dowhy.do_sampler import DoSampler
from dowhy.utils.propensity_score import state_propensity_score


class MultivariateWeightingSampler(DoSampler):
    def __init__(self, data, identified_estimand, treatments, outcomes, *args, params=None, variable_types=None,
                 num_cores=1, keep_original_treatment=False, **kwargs):
        """
        g, df, data_types

        """
        super().__init__(data, identified_estimand, treatments, outcomes, params=params,
                         variable_types=variable_types, num_cores=num_cores,
                         keep_original_treatment=keep_original_treatment)

        self.logger.info("Using MultivariateWeightingSampler for do sampling.")
        self.logger.info("Caution: do samplers assume iid data.")

        self.point_sampler = False

    def make_treatment_effective(self, x):