code: def identify_backdoor(
      graph: nx.DiGraph,
      action_nodes: List[str],
      outcome_nodes: List[str],
      observed_nodes: List[str],
      backdoor_adjustment: BackdoorAdjustment,
      include_unobserved: bool = False,
      dseparation_algo: str = "default",
      direct_effect: bool = False,
  ):
      backdoor_sets = []
      backdoor_paths = None
      bdoor_graph = None
      observed_nodes = set(observed_nodes)
      if dseparation_algo == "naive":
          backdoor_paths = get_backdoor_paths(
              graph, action_nodes, outcome_nodes
          )
      elif dseparation_algo == "default":
          bdoor_graph = do_surgery(
              graph,
              action_nodes,
              target_node_names=outcome_nodes,
              remove_outgoing_edges=True,
              remove_only_direct_edges_to_target=direct_effect,
          )
      else:
          raise ValueError(
              f"d-separation algorithm {dseparation_algo} is not supported"
          )
      backdoor_adjustment = (
          backdoor_adjustment
          if backdoor_adjustment != BackdoorAdjustment.BACKDOOR_DEFAULT
          else DEFAULT_BACKDOOR_METHOD
      )
  
      # First, checking if empty set is a valid backdoor set
      empty_set = set()
      check = check_valid_backdoor_set(
          graph,
          action_nodes,
          outcome_nodes,
          empty_set,
          backdoor_paths=backdoor_paths,
          new_graph=bdoor_graph,
          dseparation_algo=dseparation_algo,
      )
      if check["is_dseparated"]:
          backdoor_sets.append({"backdoor_set": empty_set})
          # If the method is `minimal-adjustment`, return the empty set right away.
          if backdoor_adjustment == BackdoorAdjustment.BACKDOOR_MIN:
              return backdoor_sets
  
      # Second, checking for all other sets of variables. If include_unobserved is false, then only observed variables are eligible.
      eligible_variables = (
          set(
              [
                  node
                  for node in graph.nodes
                  if include_unobserved or node in observed_nodes
              ]
          )
          - set(action_nodes)
          - set(outcome_nodes)
      )
  
      if direct_effect:
          # only remove descendants of Y
          # also allow any causes of Y that are not caused by T (for lower variance)
          eligible_variables -= get_descendants(graph, outcome_nodes)
      else:
          # remove descendants of T (mediators) and descendants of Y
          eligible_variables -= get_descendants(graph, action_nodes)
      # If var is d-separated from both treatment or outcome, it cannot
      # be a part of the backdoor set
      filt_eligible_variables = set()
      for var in eligible_variables:
          dsep_treat_var = check_dseparation(
              graph, action_nodes, parse_state(var), set()
          )
          dsep_outcome_var = check_dseparation(
              graph, outcome_nodes, parse_state(var), set()
          )
          if not dsep_outcome_var or not dsep_treat_var:
              filt_eligible_variables.add(var)
      if backdoor_adjustment in METHOD_NAMES:
          (
              backdoor_sets,
              found_valid_adjustment_set,
          ) = find_valid_adjustment_sets(
              graph,
              action_nodes,
              outcome_nodes,
              observed_nodes,
              backdoor_paths,
              bdoor_graph,
              dseparation_algo,
              backdoor_sets,
              filt_eligible_variables,
              backdoor_adjustment=backdoor_adjustment,
              max_iterations=MAX_BACKDOOR_ITERATIONS,
          )
          if (
              backdoor_adjustment == BackdoorAdjustment.BACKDOOR_DEFAULT
              and found_valid_adjustment_set
          ):
              # repeat the above search with BACKDOOR_MIN
              backdoor_sets, _ = find_valid_adjustment_sets(
                  graph,
                  action_nodes,
                  outcome_nodes,
                  observed_nodes,
                  backdoor_paths,
                  bdoor_graph,
                  dseparation_algo,
                  backdoor_sets,
                  filt_eligible_variables,
                  backdoor_adjustment=BackdoorAdjustment.BACKDOOR_MIN,
                  max_iterations=MAX_BACKDOOR_ITERATIONS,
              )
      else:
          raise ValueError(
              f"Identifier method {backdoor_adjustment} not supported. Try one of the following: {METHOD_NAMES}"
          )
      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 6 for calling a global function with example usage: identify_backdoor(graph, action_nodes, outcome_nodes, observed_nodes, backdoor_adjustment, include_unobserved, dseparation_algo, direct_effect) and returns: backdoor_sets, backdoor_sets