code: def test_identify_frontdoor_causal_model(
      self,
      example_frontdoor_graph_solution: IdentificationTestFrontdoorGraphSolution,
  ):
      graph = example_frontdoor_graph_solution.graph
      expected_sets = (
          example_frontdoor_graph_solution.valid_frontdoor_sets
      )
      invalid_sets = (
          example_frontdoor_graph_solution.invalid_frontdoor_sets
      )
      observed_nodes = example_frontdoor_graph_solution.observed_nodes
      # Building the causal model
      num_samples = 10
      df = pd.DataFrame(
          np.random.random((num_samples, len(observed_nodes))),
          columns=observed_nodes,
      )
      model = CausalModel(
          data=df, treatment="X", outcome="Y", graph=graph
      )
      estimand = model.identify_effect()
      frontdoor_set = estimand.frontdoor_variables
      assert (
          (len(frontdoor_set) == 0) and (len(expected_sets) == 0)
      ) or (  # No adjustments exist and that's expected.
          (set(frontdoor_set) in expected_sets)
          and (set(frontdoor_set) not in invalid_sets)
      )
  
file_path: dowhy/tests/causal_identifiers/test_frontdoor_identifier.py
imports: import numpy
  import pandas
  import pytest
  from dowhy import CausalModel
  from dowhy.causal_identifier import AutoIdentifier, BackdoorAdjustment
  from dowhy.causal_identifier.auto_identifier import identify_frontdoor
  from dowhy.causal_identifier.identify_effect import EstimandType
  from .base import IdentificationTestFrontdoorGraphSolution, example_frontdoor_graph_solution
label_desc: Use image node_id 2 for calling the TestFrontdoorIdentification obj's underlying member method code with example usage: obj.test_identify_frontdoor_causal_model(example_frontdoor_graph_solution) without return types