code: def rollout_and_examine(self, model, num_samples):
      assert not model.training, "You need to call model.eval()."
  
      num_total_size = 0
      num_valid_size = 0
      num_cycle = 0
      num_valid = 0
      plot_times = 0
      adj_lists_to_plot = []
  
      for i in range(num_samples):
          sampled_graph = model()
          if isinstance(sampled_graph, list):
              # When the model is a batched implementation, a list of
              # DGLGraph objects is returned. Note that with model(),
              # we generate a single graph as with the non-batched
              # implementation. We actually support batched generation
              # during the inference so feel free to modify the code.
              sampled_graph = sampled_graph[0]
  
          sampled_adj_list = dglGraph_to_adj_list(sampled_graph)
          adj_lists_to_plot.append(sampled_adj_list)
  
          graph_size = sampled_graph.num_nodes()
          valid_size = self.v_min <= graph_size <= self.v_max
          cycle = is_cycle(sampled_graph)
  
          num_total_size += graph_size
  
          if valid_size:
              num_valid_size += 1
  
          if cycle:
              num_cycle += 1
  
          if valid_size and cycle:
              num_valid += 1
  
          if len(adj_lists_to_plot) >= 4:
              plot_times += 1
              fig, ((ax0, ax1), (ax2, ax3)) = plt.subplots(2, 2)
              axes = {0: ax0, 1: ax1, 2: ax2, 3: ax3}
              for i in range(4):
                  nx.draw_circular(
                      nx.from_dict_of_lists(adj_lists_to_plot[i]),
                      with_labels=True,
                      ax=axes[i],
                  )
  
              plt.savefig(self.dir + "/samples/{:d}".format(plot_times))
              plt.close()
  
              adj_lists_to_plot = []
  
      self.num_samples_examined = num_samples
      self.average_size = num_total_size / num_samples
      self.valid_size_ratio = num_valid_size / num_samples
      self.cycle_ratio = num_cycle / num_samples
      self.valid_ratio = num_valid / num_samples
  
imports: import os
  import pickle
  import random
  import matplotlib.pyplot
  import networkx
  from torch.utils.data import Dataset
label_desc: Use image node_id 2 for calling the CycleModelEvaluation obj's underlying member method code with example usage: obj.rollout_and_examine(model, num_samples) without return types