code: def test_known_causal_model_node_is_correctly_identified():
      causal_model = create_causal_model_from_equations(
          """
      A = norm(loc=0,scale=0.1)
      B = norm(loc=0, scale=0.1)
      Y = 0.5*B + 2*A+ norm(loc=0, scale=0.1)
      Z->Y,A
      C = exp(A) + 5 * Z + parametric()
      """
      )
      list_of_nodes = {"A", "B", "C", "Z", "Y"}
      list_of_nodes_from_graph = set(causal_model.graph.nodes)
      assert list_of_nodes.issubset(
          list_of_nodes_from_graph
      ) and list_of_nodes_from_graph.issubset(list_of_nodes)
      assert isinstance(
          causal_model.causal_mechanism("C"), ConditionalStochasticModel
      )
  
file_path: dowhy/tests/gcm/test_equation_parser.py
imports: import networkx
  import numpy
  import pandas
  from flaky import flaky
  from dowhy.gcm import AdditiveNoiseModel, EmpiricalDistribution, ProbabilisticCausalModel, create_causal_model_from_equations, fit
  from dowhy.gcm.causal_mechanisms import ConditionalStochasticModel
  from dowhy.gcm.ml import create_linear_regressor_with_given_parameters
label_desc: Use image node_id 4 for calling a global function with example usage: test_known_causal_model_node_is_correctly_identified() without return types