code: def get_new_params(
      current_param_values, hyperparam_search_space, params_to_modify
  ):
      for param, _ in params_to_modify.items():
          if param in hyperparam_search_space:
              if hyperparam_search_space[param][SPACE] == "choice":
                  current_param_values[param] = hyperparam_search_space[
                      param
                  ]["categories"][-1]
              else:
                  current_param_values[param] = hyperparam_search_space[
                      param
                  ]["upper"]
      return current_param_values
  
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 7 for calling a global function with example usage: get_new_params(current_param_values, hyperparam_search_space, params_to_modify) and returns: current_param_values