code: def create_automl_config_for_features(
      features_config: ModelConfigDict,
      dataset_info: DatasetInfo,
      target: Union[str, List[str]],
      time_limit_s: Union[int, float],
      tune_for_memory: bool = False,
      user_config: Dict = None,
      random_seed: int = default_random_seed,
      imbalance_threshold: float = 0.9,
      use_reference_config: bool = False,
      backend: Union[Backend, str] = None,
  ) -> ModelConfigDict:
      default_configs = create_default_config(
          features_config,
          dataset_info,
          target,
          time_limit_s,
          random_seed,
          imbalance_threshold,
          backend,
      )
      model_config, _, _ = _model_select(
          dataset_info,
          default_configs,
          user_config,
          use_reference_config,
      )
  
      if tune_for_memory:
          warnings.warn(
              "`tune_for_memory=True` is deprecated, `batch_size=auto` will be used instead"
          )
  
      return model_config
  
imports: import argparse
  import copy
  import logging
  import os
  import warnings
  from typing import Any, Dict, List, Optional, Union
  import numpy
  import pandas
  import yaml
  from ludwig.api import LudwigModel
  from ludwig.api_annotations import PublicAPI
  from ludwig.automl.base_config import create_default_config, DatasetInfo, get_dataset_info, get_features_config, get_reference_configs
  from ludwig.backend import Backend, initialize_backend
  from ludwig.constants import AUTO, AUTOML_DEFAULT_IMAGE_ENCODER, AUTOML_DEFAULT_TABULAR_MODEL, AUTOML_DEFAULT_TEXT_ENCODER, BINARY, CATEGORY, ENCODER, HYPEROPT, IMAGE, INPUT_FEATURES, NAME, NUMBER, OUTPUT_FEATURES, TABULAR, TEXT, TRAINER, TYPE
  from ludwig.contrib import add_contrib_callback_args
  from ludwig.data.cache.types import CacheableDataset
  from ludwig.datasets import load_dataset_uris
  from ludwig.globals import LUDWIG_VERSION
  from ludwig.hyperopt.run import hyperopt
  from ludwig.schema.model_config import ModelConfig
  from ludwig.types import ModelConfigDict
  from ludwig.utils.automl.ray_utils import _ray_init
  from ludwig.utils.automl.utils import _add_transfer_config, get_model_type, set_output_feature_metric
  from ludwig.utils.data_utils import load_dataset, use_credentials
  from ludwig.utils.defaults import default_random_seed
  from ludwig.utils.fs_utils import open_file
  from ludwig.utils.heuristics import get_auto_learning_rate
  from ludwig.utils.misc_utils import merge_dict
  from ludwig.utils.print_utils import print_ludwig
label_desc: Use image node_id 3 for calling a global function with example usage: create_automl_config_for_features(features_config, dataset_info, target, time_limit_s, tune_for_memory, user_config, random_seed, imbalance_threshold, use_reference_config, backend) and returns: model_config