code: def validate(self, config: "ModelConfig"):  # noqa: F821
      features = [f for f in config.input_features] + [
          f for f in config.output_features
      ]
      feature_cols = {f.column for f in features}
      if self.column not in feature_cols:
          logging.info(
              f"Stratify column {self.column} is not among the features. "
              f"Cannot establish if it is a binary or category feature."
          )
      elif [f for f in features if f.column == self.column][
          0
      ].type not in {BINARY, CATEGORY}:
          raise ConfigValidationError(
              f"Feature for stratify column {self.column} must be binary or category"
          )
  
imports: import logging
  from abc import ABC, abstractmethod
  from typing import List, Optional, Tuple, TYPE_CHECKING
  from zlib import crc32
  import numpy
  from sklearn.model_selection import train_test_split
  from ludwig.api_annotations import DeveloperAPI
  from ludwig.backend.base import Backend
  from ludwig.constants import BINARY, CATEGORY, DATE, MIN_DATASET_SPLIT_ROWS, SPLIT
  from ludwig.error import ConfigValidationError
  from ludwig.schema.split import DateTimeSplitConfig, FixedSplitConfig, HashSplitConfig, RandomSplitConfig, StratifySplitConfig
  from ludwig.types import ModelConfigDict, PreprocessingConfigDict
  from ludwig.utils.data_utils import hash_dict, split_dataset_ttv
  from ludwig.utils.defaults import default_random_seed
  from ludwig.utils.registry import Registry
  from ludwig.utils.types import DataFrame
label_desc: Use image node_id 3 for calling the StratifySplitter obj's underlying member method code with example usage: obj.validate(config) without return types