code: def model_factory(
      model,
      path,
      problem_type,
      eval_metric,
      name_suffix=None,
      ensemble_type=StackerEnsembleModel,
      ensemble_kwargs=None,
      invalid_name_set=None,
      level=1,
  ):
      if invalid_name_set is None:
          invalid_name_set = set()
      model_type = model[AG_ARGS]["model_type"]
      if not inspect.isclass(model_type):
          model_type = MODEL_TYPES[model_type]
      name_orig = model[AG_ARGS].get("name", None)
      if name_orig is None:
          name_main = model[AG_ARGS].get(
              "name_main",
              DEFAULT_MODEL_NAMES.get(model_type, model_type.__name__),
          )
          name_prefix = model[AG_ARGS].get("name_prefix", "")
          name_suff = model[AG_ARGS].get("name_suffix", "")
          name_orig = name_prefix + name_main + name_suff
      name_stacker = None
      num_increment = 2
      if name_suffix is None:
          name_suffix = ""
      if ensemble_kwargs is None:
          name = f"{name_orig}{name_suffix}"
          while name in invalid_name_set:  # Ensure name is unique
              name = f"{name_orig}_{num_increment}{name_suffix}"
              num_increment += 1
      else:
          name = name_orig
          name_bag_suffix = model[AG_ARGS].get(
              "name_bag_suffix", "_BAG"
          )
          name_stacker = (
              f"{name}{name_bag_suffix}_L{level}{name_suffix}"
          )
          while (
              name_stacker in invalid_name_set
          ):  # Ensure name is unique
              name = f"{name_orig}_{num_increment}"
              name_stacker = (
                  f"{name}{name_bag_suffix}_L{level}{name_suffix}"
              )
              num_increment += 1
      model_params = copy.deepcopy(model)
      model_params.pop(AG_ARGS, None)
      model_params.pop(AG_ARGS_ENSEMBLE, None)
  
      model_init_kwargs = dict(
          path=path,
          name=name,
          problem_type=problem_type,
          eval_metric=eval_metric,
          hyperparameters=model_params,
      )
  
      if ensemble_kwargs is not None:
          ensemble_kwargs_model = copy.deepcopy(ensemble_kwargs)
          extra_ensemble_hyperparameters = copy.deepcopy(
              model.get(AG_ARGS_ENSEMBLE, dict())
          )
          ensemble_kwargs_model[
              "hyperparameters"
          ] = ensemble_kwargs_model.get("hyperparameters", {})
          if ensemble_kwargs_model["hyperparameters"] is None:
              ensemble_kwargs_model["hyperparameters"] = {}
          ensemble_kwargs_model["hyperparameters"].update(
              extra_ensemble_hyperparameters
          )
          model_init = ensemble_type(
              path=path,
              name=name_stacker,
              model_base=model_type,
              model_base_kwargs=model_init_kwargs,
              **ensemble_kwargs_model,
          )
      else:
          model_init = model_type(**model_init_kwargs)
  
      return model_init
  
file_path: autogluon/tabular/src/autogluon/tabular/trainer/model_presets/presets.py
imports: import copy
  import inspect
  import logging
  from collections import defaultdict
  from autogluon.common.model_filter import ModelFilter
  from autogluon.core.constants import AG_ARGS, AG_ARGS_ENSEMBLE, AG_ARGS_FIT, BINARY, MULTICLASS, QUANTILE, REGRESSION, SOFTCLASS
  from autogluon.core.models import AbstractModel, DummyModel, GreedyWeightedEnsembleModel, SimpleWeightedEnsembleModel, StackerEnsembleModel
  from autogluon.core.trainer.utils import process_hyperparameters
  from ...models import BoostedRulesModel, CatBoostModel, FastTextModel, FigsModel, FTTransformerModel, GreedyTreeModel, HSTreeModel, ImagePredictorModel, KNNModel, LGBModel, LinearModel, MultiModalPredictorModel, NNFastAiTabularModel, RFModel, RuleFitModel, TabPFNModel, TabularNeuralNetTorchModel, TextPredictorModel, VowpalWabbitModel, XGBoostModel, XTModel
  from ...models.tab_transformer.tab_transformer_model import TabTransformerModel
  from .presets_custom import get_preset_custom
label_desc: Use image node_id 4 for calling a global function with example usage: model_factory(model, path, problem_type, eval_metric, name_suffix, ensemble_type, ensemble_kwargs, invalid_name_set, level) and returns: model_init