code: def test_graph_input(
      self, beta, num_instruments, num_samples, num_treatments
  ):
      num_common_causes = 5
      data = dowhy.datasets.linear_dataset(
          beta=beta,
          num_common_causes=num_common_causes,
          num_instruments=num_instruments,
          num_samples=num_samples,
          num_treatments=num_treatments,
          treatment_is_binary=True,
      )
  
      model = CausalModel(
          data=data["df"],
          treatment=data["treatment_name"],
          outcome=data["outcome_name"],
          graph=data["gml_graph"],
          proceed_when_unidentifiable=True,
          test_significance=None,
      )
      # removing two common causes
      gml_str = 'graph[directed 1 node[ id "{0}" label "{0}"]node[ id "{1}" label "{1}"]node[ id "Unobserved Confounders" label "Unobserved Confounders"]edge[source "{0}" target "{1}"]edge[source "Unobserved Confounders" target "{0}"]edge[source "Unobserved Confounders" target "{1}"]node[ id "X0" label "X0"] edge[ source "X0" target "{0}"] node[ id "X1" label "X1"] edge[ source "X1" target "{0}"] node[ id "X2" label "X2"] edge[ source "X2" target "{0}"] edge[ source "X0" target "{1}"] edge[ source "X1" target "{1}"] edge[ source "X2" target "{1}"] node[ id "Z0" label "Z0"] edge[ source "Z0" target "{0}"]]'.format(
          data["treatment_name"][0], data["outcome_name"]
      )
      print(gml_str)
      model = CausalModel(
          data=data["df"],
          treatment=data["treatment_name"],
          outcome=data["outcome_name"],
          graph=gml_str,
          proceed_when_unidentifiable=True,
          test_significance=None,
          missing_nodes_as_confounders=True,
      )
      common_causes = model.get_common_causes()
      assert all(
          node_name in common_causes for node_name in ["X1", "X2"]
      )
  
imports: import networkx
  import pandas
  import pytest
  from flaky import flaky
  from pytest import mark
  from sklearn import linear_model
  import dowhy
  import dowhy.datasets
  from dowhy import CausalModel
  from dowhy.causal_graph import CausalGraph
  from dowhy.gcm import ProbabilisticCausalModel, StructuralCausalModel
  from dowhy.utils.graph_operations import daggity_to_dot
label_desc: Use image node_id 2 for calling the TestCausalModel obj's underlying member method code with example usage: obj.test_graph_input(beta, num_instruments, num_samples, num_treatments) without return types