class_docstr: Event topic class with logging statistics based only on Gym space actions,
  therefore applicable to any valid reinforcement learning environment.
class_docstr_tok: ["Event","topic","class","with","logging","statistics","based","only","on","Gym","space","actions",",","therefore","applicable","to","any","valid","reinforcement","learning","environment","."]
docstr: action taken 
docstr_tok: ["action","taken"]
code: def multi_binary_action(
      self,
      step_key: str,
      agent_name: str,
      name: str,
      value: int,
      num_binary_actions: int,
  ):
      """action taken"""
  
file_path: maze/maze/core/log_events/monitoring_events.py
imports: from abc import ABC
  import numpy
  from maze.core.log_events.log_create_figure_functions import create_categorical_plot, create_violin_distribution, create_binary_plot
  from maze.core.log_stats.event_decorators import define_episode_stats, define_step_stats, define_epoch_stats, define_stats_grouping, define_plot
  from maze.core.log_stats.reducer_functions import histogram
label_desc: Use image node_id 1 for calling the ActionEvents obj's underlying member method code with example usage: obj.multi_binary_action(step_key, agent_name, name, value, num_binary_actions) without return types