code: def test_5(self):
      treatment = "T"
      outcome = "Y"
      variables = ["X1", "X2", "X3", "X4"]
      causal_graph = (
          "digraph{X1->T;X1->X2;X2->Y;X3->T;X3->X4;X4->Y;T->Y}"
      )
  
      vars = list(treatment) + list(outcome) + list(variables)
      df = pd.DataFrame(columns=vars)
  
      treatment_name = parse_state(treatment)
      outcome_name = parse_state(outcome)
  
      # Causal model initialization
      causal_model = CausalModel(
          df, treatment, outcome, graph=causal_graph
      )
  
      # Obtain backdoor sets
      path = Backdoor(
          causal_model._graph._graph, treatment_name, outcome_name
      )
      backdoor_sets = path.get_backdoor_vars()
  
      # Check if backdoor sets are valid i.e. if they block all paths between the treatment and the outcome
      backdoor_paths = causal_model._graph.get_backdoor_paths(
          treatment_name, outcome_name
      )
      check_set = set(backdoor_sets[0]["backdoor_set"])
      check = causal_model._graph.check_valid_backdoor_set(
          treatment_name,
          outcome_name,
          check_set,
          backdoor_paths=backdoor_paths,
      )
  
      assert check["is_dseparated"]
  
file_path: dowhy/tests/causal_identifiers/test_optimize_backdoor.py
imports: import pandas
  from dowhy import CausalModel
  from dowhy.causal_identifier.backdoor import Backdoor
  from dowhy.utils.api import parse_state
label_desc: Use image node_id 5 for calling the TestOptimizeBackdoorIdentifier obj's underlying member method code with example usage: obj.test_5() without return types