code: def test_update_increase_batch_size_on_plateau_max():
      old_valid_config = {
          "input_features": [
              {"name": "input_feature_1", "type": "category"}
          ],
          "output_features": [
              {"name": "output_feature_1", "type": "category"}
          ],
          "trainer": {
              "increase_batch_size_on_plateau_max": 256,
          },
      }
  
      updated_config = upgrade_config_dict_to_latest_version(
          old_valid_config
      )
      del updated_config["ludwig_version"]
  
      expected_config = copy.deepcopy(old_valid_config)
      del expected_config["trainer"][
          "increase_batch_size_on_plateau_max"
      ]
      expected_config["trainer"]["max_batch_size"] = 256
  
      assert updated_config == expected_config
  
imports: import copy
  import math
  from typing import Any, Dict
  import pytest
  from ludwig.constants import BATCH_SIZE, BFILL, CLASS_WEIGHTS, DEFAULTS, EVAL_BATCH_SIZE, EXECUTOR, HYPEROPT, INPUT_FEATURES, LEARNING_RATE_SCHEDULER, LOSS, NUMBER, OUTPUT_FEATURES, PREPROCESSING, SCHEDULER, SPLIT, TRAINER, TYPE
  from ludwig.schema.model_config import ModelConfig
  from ludwig.schema.trainer import ECDTrainerConfig
  from ludwig.utils.backward_compatibility import _update_backend_cache_credentials, _upgrade_encoder_decoder_params, _upgrade_feature, _upgrade_preprocessing_split, upgrade_config_dict_to_latest_version, upgrade_missing_value_strategy, upgrade_model_progress
  from ludwig.utils.trainer_utils import TrainerMetric
label_desc: Use image node_id 9 for calling a global function with example usage: test_update_increase_batch_size_on_plateau_max() without return types