code: def __init__(
      self,
      verbosity: int = 0,
      eval_metric: Optional[str] = None,
      path: Optional[str] = None,
      model_persistence_max_memory: Optional[float] = None,
      **fit_parameters,
  ) -> None:
      self._verbosity = verbosity
      self._fit_parameters = fit_parameters
      self._eval_metric = eval_metric
      self._model_persistence_max_memory = model_persistence_max_memory
  
      self._feature_names = None
      self._target_column = ["Y"]
  
      self._auto_gluon_model = tabular.TabularPredictor(
          label=self._target_column[0],
          verbosity=self._verbosity,
          eval_metric=self._eval_metric,
          path=path,
      )
  
      if "hyperparameters" not in self._fit_parameters:
          self._fit_parameters[
              "hyperparameters"
          ] = get_hyperparameter_config("light")
          self._fit_parameters["hyperparameters"].update({"LR": {}})
  
      if "infer_limit" not in self._fit_parameters:
          self._fit_parameters["infer_limit"] = 0.005
  
      if version.parse(tabular.version.__version__) >= version.parse(
          "0.4"
      ):
          if "presets" not in self._fit_parameters:
              self._fit_parameters["presets"] = [
                  "optimize_for_deployment"
              ]
  
imports: from typing import Any, Dict, List, Optional
  import numpy
  import pandas
  from autogluon import tabular
  from autogluon.tabular.configs.hyperparameter_configs import get_hyperparameter_config
  from packaging import version
  from dowhy.gcm.ml import ClassificationModel, PredictionModel
  from dowhy.gcm.util.general import shape_into_2d
label_desc: Use image node_id 1 to create a new _AutoGluonModel object from inherited base classes: PredictionModel with example: obj = _AutoGluonModel(verbosity, eval_metric, path, model_persistence_max_memory)