code: def get_validation_method(
      get_finetuning_validator: Callable[
          [bool, bool], FinetuningValidator
      ]
  ) -> Callable[
      [bool, bool, bool, bool, GraphSchema], ValidationMethodType
  ]:
      def inner(
          finetuning: bool,
          load: bool,
          nlu: bool,
          core: bool,
          graph_schema: Optional[GraphSchema] = None,
      ) -> ValidationMethodType:
          validator = get_finetuning_validator(
              finetuning=finetuning,
              load=load,
              config={"validate_core": core, "validate_nlu": nlu},
              graph_schema=graph_schema,
          )
  
          return validator.validate
  
      return inner
  
imports: import os
  from pathlib import Path
  import copy
  from typing import Callable, List, Optional, Text, Dict, Any
  from _pytest.monkeypatch import MonkeyPatch
  import pytest
  from rasa.engine.graph import ExecutionContext, GraphComponent, GraphSchema, SchemaNode
  from rasa.engine.storage.resource import Resource
  from rasa.engine.storage.storage import ModelStorage
  from rasa.graph_components.validators.finetuning_validator import FinetuningValidator
  from rasa.nlu.tokenizers.whitespace_tokenizer import WhitespaceTokenizer
  from rasa.core.policies.rule_policy import RulePolicy
  from rasa.shared.constants import DEFAULT_CONFIG_PATH, DEFAULT_DATA_PATH, DEFAULT_DOMAIN_PATH
  from rasa.shared.core.domain import KEY_RESPONSES, Domain
  from rasa.shared.importers.rasa import RasaFileImporter
  from rasa.shared.importers.importer import NluDataImporter, TrainingDataImporter
  import rasa.shared.utils.io
  from rasa.shared.exceptions import InvalidConfigException
  from rasa.shared.nlu.constants import ACTION_NAME, INTENT, TEXT
  from rasa.shared.nlu.training_data.message import Message
  from rasa.shared.nlu.training_data.training_data import TrainingData
label_desc: Use image node_id 3 for calling a global function with example usage: get_validation_method(get_finetuning_validator) and returns: inner, validator