code: def get_decision_sequence(size):
      """
      Get the decision sequence for generating valid cycles with DGMG for teacher
      forcing optimization.
      """
      decision_sequence = []
  
      for i in range(size):
          decision_sequence.append(0)  # Add node
  
          if i != 0:
              decision_sequence.append(0)  # Add edge
              decision_sequence.append(
                  i - 1
              )  # Set destination to be previous node.
  
          if i == size - 1:
              decision_sequence.append(0)  # Add edge
              decision_sequence.append(
                  0
              )  # Set destination to be the root.
  
          decision_sequence.append(1)  # Stop adding edge
  
      decision_sequence.append(1)  # Stop adding node
  
      return decision_sequence
  
imports: import os
  import pickle
  import random
  import matplotlib.pyplot
  import networkx
  from torch.utils.data import Dataset
label_desc: Use image node_id 4 for calling a global function with example usage: get_decision_sequence(size) and returns: decision_sequence