code: def test_graph_input3(
      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 = """dag {
          "Unobserved Confounders" [pos="0.491,-1.056"]
          X0 [pos="-2.109,0.057"]
          X1 [adjusted, pos="-0.453,-1.562"]
          X2 [pos="-2.268,-1.210"]
          Z0 [pos="-1.918,-1.735"]
          v0 [latent, pos="-1.525,-1.293"]
          y [outcome, pos="-1.164,-0.116"]
          "Unobserved Confounders" -> v0
          "Unobserved Confounders" -> y
          X0 -> v0
          X0 -> y
          X1 -> v0
          X1 -> y
          X2 -> v0
          X2 -> y
          Z0 -> v0
          v0 -> y
          }
          """
      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"]
      )
      all_nodes = model._graph.get_all_nodes(include_unobserved=True)
      assert all(
          node_name in all_nodes
          for node_name in [
              "Unobserved Confounders",
              "X0",
              "X1",
              "X2",
              "Z0",
              "v0",
              "y",
          ]
      )
      all_nodes = model._graph.get_all_nodes(include_unobserved=False)
      assert "Unobserved Confounders" not in all_nodes
  
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 4 for calling the TestCausalModel obj's underlying member method code with example usage: obj.test_graph_input3(beta, num_instruments, num_samples, num_treatments) without return types