code: def _update_text_encoder(
      input_features: List, old_text_encoder: str, new_text_encoder: str
  ) -> None:
      for feature in input_features:
          if (
              feature["type"] == TEXT
              and feature["encoder"] == old_text_encoder
          ):
              feature["encoder"] = new_text_encoder
  
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 8 for calling a global function with example usage: _update_text_encoder(input_features, old_text_encoder, new_text_encoder) without return types