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 _fit(self, X, y, **kwargs):
      X = self.preprocess(X)
      model_cls = self._get_model_type()
      model_params = self._get_model_params()
      if "raise" in model_params:
          raise_msg = model_params.get("raise_msg", "")
          raise model_params["raise"](raise_msg)
      if model_cls == DummyQuantileRegressor:
          self.model = model_cls(quantile_levels=self.quantile_levels)
      else:
          self.model = model_cls()
      self.model.fit(X=X, y=y)
  
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 3 for calling the DummyModel obj's underlying member method code with example usage: obj._fit(X, y) without return types