code: def check_param_compatibility(parameters, dataset_filepaths):
      """
      Check parameters are compatible.
      """
      # Check mode of operation
      if parameters["train_model"]:
          if (
              "train" not in dataset_filepaths
              or "valid" not in dataset_filepaths
          ):
              msg = """If train_model is set to True, both train and valid set must exist 
                  in the specified dataset folder: {0}""".format(
                  parameters["dataset_text_folder"]
              )
              raise IOError(msg)
      elif parameters["use_pretrained_model"]:
          if (
              "train" in dataset_filepaths
              and "valid" in dataset_filepaths
          ):
              msg = """WARNING: train and valid set exist in the specified dataset folder, 
                  but train_model is set to FALSE: {0}""".format(
                  parameters["dataset_text_folder"]
              )
              print(msg)
          if (
              "test" not in dataset_filepaths
              and "deploy" not in dataset_filepaths
          ):
              msg = """For prediction mode, either test set and deploy set must exist 
                  in the specified dataset folder: {0}""".format(
                  parameters["dataset_text_folder"]
              )
              raise IOError(msg)
      # if not parameters['train_model'] and not parameters['use_pretrained_model']:
      else:
          raise ValueError(
              "At least one of train_model and use_pretrained_model must be set to True."
          )
  
      if parameters["use_pretrained_model"]:
          if all(
              [
                  not parameters[s]
                  for s in [
                      "reload_character_embeddings",
                      "reload_character_lstm",
                      "reload_token_embeddings",
                      "reload_token_lstm",
                      "reload_feedforward",
                      "reload_crf",
                  ]
              ]
          ):
              msg = """If use_pretrained_model is set to True, at least one of reload_character_embeddings, 
                  reload_character_lstm, reload_token_embeddings, reload_token_lstm, reload_feedforward, 
                  reload_crf must be set to True."""
              raise ValueError(msg)
  
      if parameters["gradient_clipping_value"] < 0:
          parameters["gradient_clipping_value"] = abs(
              parameters["gradient_clipping_value"]
          )
  
      try:
          if parameters["output_scores"] and parameters["use_crf"]:
              warn_msg = """Warning when use_crf is True, scores are decoded
              using the crf. As a result, the scores cannot be directly interpreted
              in terms of class prediction.
              """
              warnings.warn(warn_msg)
      except KeyError:
          parameters["output_scores"] = False
  
imports: import codecs
  import configparser
  import copy
  import distutils.util
  import glob
  import os
  import pickle
  from pprint import pprint
  import random
  import shutil
  import sys
  import time
  import warnings
  import pkg_resources
  import numpy
  import matplotlib
  import tensorflow
  from tensorflow.contrib.tensorboard.plugins import projector
  from neuroner import train
  from neuroner import dataset
  from neuroner.entity_lstm import EntityLSTM
  from neuroner import utils
  from neuroner import conll_to_brat
  from neuroner import evaluate
  from neuroner import brat_to_conll
  from neuroner import utils_nlp
label_desc: Use image node_id 9 for calling a global function with example usage: check_param_compatibility(parameters, dataset_filepaths) without return types