code: def _get_text_feature_min_usable_length(
      input_features: List, training_set_metadata
  ) -> int:
      """Returns min of AUTOML_SMALLER_TEXT_LENGTH and lowest 99th percentile sequence length over text features."""
      min_usable_length = AUTOML_SMALLER_TEXT_LENGTH
      for feature in input_features:
          if feature["type"] == TEXT:
              feature_99ptile_len = training_set_metadata[
                  feature["name"]
              ]["max_sequence_length_99ptile"]
              if feature_99ptile_len < min_usable_length:
                  min_usable_length = feature_99ptile_len
      return round(min_usable_length)
  
imports: import copy
  import logging
  import math
  from collections import OrderedDict
  from typing import List
  import psutil
  from ludwig.api import LudwigModel
  from ludwig.backend import initialize_backend
  from ludwig.constants import AUTO, AUTOML_DEFAULT_TEXT_ENCODER, AUTOML_LARGE_TEXT_DATASET, AUTOML_MAX_ROWS_PER_CHECKPOINT, AUTOML_SMALLER_TEXT_ENCODER, AUTOML_SMALLER_TEXT_LENGTH, AUTOML_TEXT_ENCODER_MAX_TOKEN_LEN, HYPEROPT, MINIMUM_BATCH_SIZE, PREPROCESSING, SPACE, TEXT, TRAINER
  from ludwig.data.preprocessing import preprocess_for_training
  from ludwig.features.feature_registries import update_config_with_metadata
  from ludwig.schema.model_config import ModelConfig
  from ludwig.utils.automl.utils import get_model_type
  from ludwig.utils.torch_utils import initialize_pytorch
label_desc: Use image node_id 9 for calling a global function with example usage: _get_text_feature_min_usable_length(input_features, training_set_metadata) and returns: round