code: def visualize_stories(
      story_steps: List[StoryStep],
      domain: Domain,
      output_file: Optional[Text],
      max_history: int,
      nlu_training_data: Optional["TrainingData"] = None,
      should_merge_nodes: bool = True,
      fontsize: int = 12,
  ) -> "networkx.MultiDiGraph":
      """Given a set of stories, generates a graph visualizing the flows in the stories.
  
      Visualization is always a trade off between making the graph as small as
      possible while
      at the same time making sure the meaning doesn't change to "much". The
      algorithm will
      compress the graph generated from the stories to merge nodes that are
      similar. Hence,
      the algorithm might create paths through the graph that aren't actually
      specified in the
      stories, but we try to minimize that.
  
      Output file defines if and where a file containing the plotted graph
      should be stored.
  
      The history defines how much 'memory' the graph has. This influences in
      which situations the
      algorithm will merge nodes. Nodes will only be merged if they are equal
      within the history, this
      means the larger the history is we take into account the less likely it
      is we merge any nodes.
  
      The training data parameter can be used to pass in a Rasa NLU training
      data instance. It will
      be used to replace the user messages from the story file with actual
      messages from the training data.
      """
      story_graph = StoryGraph(story_steps)
  
      g = TrainingDataGenerator(
          story_graph,
          domain,
          use_story_concatenation=False,
          tracker_limit=100,
          augmentation_factor=0,
      )
      completed_trackers = g.generate()
      event_sequences = [t.events for t in completed_trackers]
  
      graph = visualize_neighborhood(
          None,
          event_sequences,
          output_file,
          max_history,
          nlu_training_data,
          should_merge_nodes,
          max_distance=1,
          fontsize=fontsize,
      )
      return graph
  
imports: from collections import defaultdict, deque
  import random
  from typing import Any, Text, List, Deque, Dict, Optional, Set, TYPE_CHECKING, Union, cast
  import rasa.shared.utils.io
  from rasa.shared.constants import INTENT_MESSAGE_PREFIX
  from rasa.shared.core.constants import ACTION_LISTEN_NAME
  from rasa.shared.core.domain import Domain
  from rasa.shared.core.events import UserUttered, ActionExecuted, Event
  from rasa.shared.core.generator import TrainingDataGenerator
  from rasa.shared.core.training_data.structures import StoryGraph, StoryStep
  from rasa.shared.nlu.constants import ENTITY_ATTRIBUTE_VALUE, INTENT, TEXT, ENTITY_ATTRIBUTE_TYPE, INTENT_NAME_KEY
label_desc: Use image node_id 18 for calling a global function with example usage: visualize_stories(story_steps, domain, output_file, max_history, nlu_training_data, should_merge_nodes, fontsize) and returns: graph