code: def test_graph_input_nx(
      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,
      )
      nx_graph = nx.DiGraph(nx.parse_gml(data["gml_graph"]))
      model = CausalModel(
          data=data["df"],
          treatment=data["treatment_name"],
          outcome=data["outcome_name"],
          graph=nx_graph,
          proceed_when_unidentifiable=True,
          test_significance=None,
      )
      # removing two common causes
      daggity_file = "tests/sample_dag.txt"
      with open(daggity_file, "r") as text_file:
          graph_str = text_file.read()
      graph_str = daggity_to_dot(graph_str)
      graph_str = graph_str.replace("\n", " ")
      import pygraphviz as pgv
  
      nx_graph2 = pgv.AGraph(graph_str, strict=True, directed=True)
      nx_graph2 = nx.drawing.nx_agraph.from_agraph(nx_graph2)
      model = CausalModel(
          data=data["df"],
          treatment=data["treatment_name"],
          outcome=data["outcome_name"],
          graph=nx_graph2,
          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 6 for calling the TestCausalModel obj's underlying member method code with example usage: obj.test_graph_input_nx(beta, num_instruments, num_samples, num_treatments) without return types