code: def identify_efficient_backdoor(
      graph: nx.DiGraph,
      action_nodes: List[str],
      outcome_nodes: List[str],
      observed_nodes: List[str],
      backdoor_adjustment: BackdoorAdjustment,
      costs: List,
      conditional_node_names: List[str] = None,
  ):
      """Method implementing algorithms to compute efficient backdoor sets, as
      described in Rotnitzky and Smucler (2020), Smucler, Sapienza and Rotnitzky (2021)
      and Smucler and Rotnitzky (2022).
  
      For backdoor_adjustment='efficient-adjustment', computes an optimal backdoor set,
      that is, a backdoor set comprised of observable variables that yields non-parametric
      estimators of the interventional mean with the smallest asymptotic variance
      among those that are based on observable backdoor sets. This optimal backdoor
      set always exists when no variables are latent, and the algorithm is guaranteed to compute
      it in this case. Under a non-parametric graphical model with latent variables,
      such a backdoor set can fail to exist. When certain sufficient conditions under which it is
      known that such a backdoor set exists are not satisfied, an error is raised.
  
      For backdoor_adjustment='efficient-minimal-adjustment', computes an optimal minimal backdoor set,
      that is, a minimal backdoor set comprised of observable variables that yields non-parametric
      estimators of the interventional mean with the smallest asymptotic variance
      among those that are based on observable minimal backdoor sets.
  
      For backdoor_adjustment='efficient-mincost-adjustment', computes an optimal minimum cost backdoor set,
      that is, a minimum cost backdoor set comprised of observable variables that yields non-parametric
      estimators of the interventional mean with the smallest asymptotic variance
      among those that are based on observable minimum cost backdoor sets. The cost
      of a backdoor set is defined as the sum of the costs of the variables that comprise it.
  
      The various optimal backdoor sets computed by this method are not only optimal under
      non-parametric graphical models and non-parametric estimators of interventional mean,
      but also under linear graphical models and OLS estimators, per results in Henckel, Perkovic
      and Maathuis (2020).
  
      :param costs: a list with non-negative costs associated with variables in the graph. Only used
      for estimatand_type='non-parametric-ate' and backdoor_adjustment='efficient-mincost-adjustment'. If
      not costs are provided by the user, and backdoor_adjustment='efficient-mincost-adjustment', costs
      are assumed to be equal to one for all variables in the graph. The structure of the list should
      be of the form [(node, {"cost": x}) for node in nodes].
      :param conditional_node_names: variables that are used to determine treatment. If none are
      provided, it is assumed that the intervention sets the treatment to a constant.
      :returns:  backdoor_sets, a list of dictionaries, with each dictionary
      having as values a backdoor set.
      """
      if (
          costs is None
          and backdoor_adjustment == "efficient-mincost-adjustment"
      ):
          logger.warning(
              "No costs were passed, so they will be assumed to be constant and equal to 1."
          )
      efficient_bd = EfficientBackdoor(
          graph=graph,
          action_nodes=action_nodes,
          outcome_nodes=outcome_nodes,
          observed_nodes=observed_nodes,
          conditional_node_names=conditional_node_names,
          costs=costs,
      )
      if backdoor_adjustment == BackdoorAdjustment.BACKDOOR_EFFICIENT:
          backdoor_set = efficient_bd.optimal_adj_set()
          backdoor_sets = [{"backdoor_set": tuple(backdoor_set)}]
      elif (
          backdoor_adjustment
          == BackdoorAdjustment.BACKDOOR_MIN_EFFICIENT
      ):
          backdoor_set = efficient_bd.optimal_minimal_adj_set()
          backdoor_sets = [{"backdoor_set": tuple(backdoor_set)}]
      elif (
          backdoor_adjustment
          == BackdoorAdjustment.BACKDOOR_MINCOST_EFFICIENT
      ):
          backdoor_set = efficient_bd.optimal_mincost_adj_set()
          backdoor_sets = [{"backdoor_set": tuple(backdoor_set)}]
      return backdoor_sets
  
imports: import itertools
  import logging
  from enum import Enum
  from typing import Dict, List, Optional, Union
  import networkx
  import sympy
  import sympy.stats
  from dowhy.causal_identifier.efficient_backdoor import EfficientBackdoor
  from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand
  from dowhy.graph import check_dseparation, check_valid_backdoor_set, check_valid_frontdoor_set, check_valid_mediation_set, do_surgery, get_all_directed_paths, get_backdoor_paths, get_descendants, get_instruments, has_directed_path
  from dowhy.utils.api import parse_state
label_desc: Use image node_id 7 for calling a global function with example usage: identify_efficient_backdoor(graph, action_nodes, outcome_nodes, observed_nodes, backdoor_adjustment, costs, conditional_node_names) and returns: backdoor_sets