code: def create_dot_graph(
      treatments,
      outcome,
      common_causes,
      instruments,
      effect_modifiers=[],
      frontdoor_variables=[],
  ):
      dot_graph = "digraph {"
      for currt in treatments:
          if len(frontdoor_variables) == 0:
              dot_graph += "{0}->{1};".format(currt, outcome)
          dot_graph += " ".join(
              [v + "-> " + currt + ";" for v in common_causes]
          )
          dot_graph += " ".join(
              [v + "-> " + currt + ";" for v in instruments]
          )
          dot_graph += " ".join(
              [currt + "-> " + v + ";" for v in frontdoor_variables]
          )
  
      dot_graph += " ".join(
          [v + "-> " + outcome + ";" for v in common_causes]
      )
      dot_graph += " ".join(
          [v + "-> " + outcome + ";" for v in effect_modifiers]
      )
      dot_graph += " ".join(
          [v + "-> " + outcome + ";" for v in frontdoor_variables]
      )
      dot_graph = dot_graph + "}"
      # Adding edges between common causes and the frontdoor mediator
      for v1 in common_causes:
          dot_graph += " ".join(
              [v1 + "-> " + v2 + ";" for v2 in frontdoor_variables]
          )
      return dot_graph
  
imports: import math
  import string
  import networkx
  import numpy
  import pandas
  import scipy.stats
  from numpy.random import choice
  from scipy.stats import bernoulli, halfnorm, poisson, uniform
  from sklearn.neural_network import MLPRegressor
  from dowhy.utils.graph_operations import add_edge, del_edge, get_random_node_pair, get_simple_ordered_tree
label_desc: Use image node_id 8 for calling a global function with example usage: create_dot_graph(treatments, outcome, common_causes, instruments, effect_modifiers, frontdoor_variables) and returns: dot_graph