code: def check_prediction_values(row, to_predict):
      try:
          for field_name, field_type in to_predict.items():
              if field_type in [int, float]:
                  print(f"checking {field_name} is int or float")
                  print(row[field_name], type(row[field_name]))
                  assert isinstance(row[field_name], (int, float))
                  print("checking min bound")
                  assert isinstance(
                      row[f"{field_name}_min"], (int, float)
                  )
                  print("checking max bound")
                  assert isinstance(
                      row[f"{field_name}_max"], (int, float)
                  )
                  print("comparing the two")
                  assert (
                      row[f"{field_name}_max"]
                      > row[f"{field_name}_min"]
                  )
              elif field_type is str:
                  print(f"checking {field_name} is str")
                  assert isinstance(row[field_name], str)
              else:
                  assert False
  
              print(f"checking confidence for {field_name}")
              assert isinstance(
                  row[f"{field_name}_confidence"], (int, float)
              )
              print(f"checking explain for {field_name}")
              assert isinstance(
                  row[f"{field_name}_explain"], (str, dict)
              )
      except Exception as e:
          print(f'Error "{e}" | Wrong values in row:')
          print(row)
          return False
      return True
  
imports: import time
  import os
  import json
  import subprocess
  import atexit
  import asyncio
  import re
  import sys
  from pathlib import Path
  import signal
  import psutil
  import requests
  from pandas import DataFrame
  from ps import wait_port, is_port_in_use, net_connections
label_desc: Use image node_id 11 for calling a global function with example usage: check_prediction_values(row, to_predict) and returns: True, False