class_docstr: The Random Forest surrogate uses a random forest model to predict model
  performances.
class_docstr_tok: ["The","Random","Forest","surrogate","uses","a","random","forest","model","to","predict","model","performances","."]
code: def _fit(
      self, X: List[Config[ModelConfig]], y: npt.NDArray[np.float32]
  ) -> None:
      X_numpy = self.config_transformer.fit_transform(X)
      self.estimator.fit(X_numpy, y)
  
file_path: gluonts/src/gluonts/nursery/tsbench/src/tsbench/surrogate/random_forest.py
imports: from typing import List, Optional
  import numpy
  import numpy.typing
  from sklearn.ensemble import RandomForestRegressor
  from sklearn.multioutput import MultiOutputRegressor
  from tsbench.config import Config, ModelConfig
  from tsbench.evaluations.tracking import ModelTracker
  from tsbench.surrogate._base import DatasetFeaturesMixin
  from ._base import DatasetFeaturesMixin, OutputNormalization, Surrogate
  from ._factory import register_surrogate
  from .transformers import ConfigTransformer
label_desc: Use image node_id 2 for calling the RandomForestSurrogate obj's underlying member method code with example usage: obj._fit(X, y) without return types