code: def create(
      self,
      target: str,
      df: Optional[pd.DataFrame] = None,
      args: Optional[dict] = None,
  ) -> None:
      config_args = args["using"]
  
      target_dtype = infer_types(df, 0).to_dict()["dtypes"][target]
  
      if target_dtype in ["binary", "categorical", "tags"]:
          config = ClassificationConfig(**config_args)
  
          model = TabularPredictor(
              label=target,
          )
      elif target_dtype in ["integer", "float", "quantity"]:
          config = RegressionConfig(**config_args)
  
          model = TabularPredictor(label=target)
  
      else:
          raise Exception("This task is not supported!")
  
      model.fit(df, **vars(config))
      self.model_storage.file_set("model", dill.dumps(model))
      self.model_storage.json_set("args", args)
  
file_path: mindsdb/mindsdb/integrations/handlers/autogluon_handler/autogluon_handler.py
imports: from typing import Optional
  import dill
  import pandas
  from autogluon.tabular import TabularPredictor
  from type_infer.api import infer_types
  from mindsdb.integrations.libs.base import BaseMLEngine
  from mindsdb.utilities import log
  from .config import ClassificationConfig, RegressionConfig
label_desc: Use image node_id 1 for calling the AutoGluonHandler obj's underlying member method code with example usage: obj.create(target, df, args) without return types