class_docstr: A dummy model that ignores input features and predicts only a constant value.
  Useful for tests and calculating worst-case performance.
code: def _get_model_type(self):
      from sklearn.dummy import DummyClassifier, DummyRegressor
  
      if self.problem_type == REGRESSION:
          return DummyRegressor
      elif self.problem_type == QUANTILE:
          return DummyQuantileRegressor
      elif self.problem_type in [BINARY, MULTICLASS]:
          return DummyClassifier
      else:
          raise ValueError(
              f"DummyModel does not support problem_type={self.problem_type}"
          )
  
file_path: autogluon/core/src/autogluon/core/models/dummy/dummy_model.py
imports: import pandas
  from ...constants import BINARY, MULTICLASS, QUANTILE, REGRESSION
  from ..None import AbstractModel
  from ._dummy_quantile_regressor import DummyQuantileRegressor
label_desc: Use image node_id 1 for calling the DummyModel obj's underlying member method code with example usage: obj._get_model_type() and returns: DummyRegressor, DummyQuantileRegressor, DummyClassifier