code: def __init__(self, db: EvaDBDatabase, node: CreateFunctionPlan):
      super().__init__(db, node)
      self.function_dir = Path(EvaDB_INSTALLATION_DIR) / "functions"
  
imports: import contextlib
  import hashlib
  import locale
  import os
  import pickle
  import re
  import time
  from pathlib import Path
  from typing import Dict, List
  import numpy
  import pandas
  from evadb.catalog.catalog_utils import get_metadata_properties
  from evadb.catalog.models.function_catalog import FunctionCatalogEntry
  from evadb.catalog.models.function_io_catalog import FunctionIOCatalogEntry
  from evadb.catalog.models.function_metadata_catalog import FunctionMetadataCatalogEntry
  from evadb.configuration.constants import DEFAULT_SKLEARN_TRAIN_MODEL, DEFAULT_TRAIN_REGRESSION_METRIC, DEFAULT_TRAIN_TIME_LIMIT, DEFAULT_XGBOOST_TASK, SKLEARN_SUPPORTED_MODELS, EvaDB_INSTALLATION_DIR
  from evadb.database import EvaDBDatabase
  from evadb.executor.abstract_executor import AbstractExecutor
  from evadb.functions.decorators.utils import load_io_from_function_decorators
  from evadb.models.storage.batch import Batch
  from evadb.plan_nodes.create_function_plan import CreateFunctionPlan
  from evadb.third_party.huggingface.create import gen_hf_io_catalog_entries
  from evadb.utils.errors import FunctionIODefinitionError
  from evadb.utils.generic_utils import load_function_class_from_file, string_comparison_case_insensitive, try_to_import_flaml_automl, try_to_import_ludwig, try_to_import_neuralforecast, try_to_import_statsforecast, try_to_import_torch, try_to_import_ultralytics
  from evadb.utils.logging_manager import logger
label_desc: Use image node_id 1 to create a new CreateFunctionExecutor object from inherited base classes: AbstractExecutor with example: obj = CreateFunctionExecutor(db, node)