code: def print_debug_information(automl):
      # In case it is called with estimator,
      # Get the automl object
      if hasattr(automl, "automl_"):
          automl = automl.automl_
  
      # Log file path
      log_file = glob.glob(
          os.path.join(
              automl._backend.temporary_directory, "AutoML*.log"
          )
      )[0]
  
      include_messages = [
          "INFO",
          "DEBUG",
          "WARN",
          "CRITICAL",
          "ERROR",
          "FATAL",
      ]
  
      # There is a lot of content in the log files. Only
      # parsing the main message and ignore the metalearning
      # messages
      try:
          with open(log_file) as logfile:
              content = logfile.readlines()
  
          # Get the messages to debug easier!
          content = [
              line
              for line in content
              if any(msg in line for msg in include_messages)
              and "metalearning" not in line
          ]
  
      except Exception as e:
          return str(e)
  
      # Also add the run history if any
      if hasattr(automl, "runhistory_") and hasattr(
          automl.runhistory_, "data"
      ):
          for k, v in automl.runhistory_.data.items():
              content += ["{}->{}".format(k, v)]
      else:
          content += ["No RunHistory"]
  
      # Also add the ensemble history if any
      if len(automl.ensemble_performance_history) > 0:
          content += [
              str(h) for h in automl.ensemble_performance_history
          ]
      else:
          content += ["No Ensemble History"]
  
      return os.linesep.join(content)
  
file_path: auto-sklearn/test/test_automl/automl_utils.py
imports: import typing
  import glob
  import os
  import re
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: print_debug_information(automl) and returns: os, str