code: def get_model_type(config: dict) -> str:
      if (
          "input_features" in config
          and len(config["input_features"]) == 1
          and "type" in config["input_features"][0]
          and config["input_features"][0]["type"] == "text"
      ):
          model_type = "text"
      elif COMBINER in config and TYPE in config[COMBINER]:
          model_type = config[COMBINER][TYPE]
      else:
          default_combiner_type = get_combiner_jsonschema()[
              "properties"
          ]["type"]["default"]
          model_type = default_combiner_type
      return model_type
  
file_path: ludwig/ludwig/utils/automl/utils.py
imports: import bisect
  import logging
  from typing import Dict
  from numpy import nan_to_num
  from pandas import Series
  from ludwig.api_annotations import DeveloperAPI
  from ludwig.constants import BINARY, CATEGORY, COMBINER, CONFIG, HYPEROPT, IMBALANCE_DETECTION_RATIO, NAME, NUMBER, PARAMETERS, SEARCH_ALG, TRAINER, TYPE
  from ludwig.features.feature_registries import get_output_type_registry
  from ludwig.modules.metric_registry import get_metric_objective
  from ludwig.schema.combiners.utils import get_combiner_jsonschema
label_desc: Use image node_id 3 for calling a global function with example usage: get_model_type(config) and returns: model_type