class_docstr: An `Estimator` that uses an external fitting mechanism, thus eliminating
  the need for a Trainer. Differs from DummyEstimator in that DummyEstimator
  does not use the training data, but merely trains at prediction time.
  
  Parameters
  ----------
  predictor_cls
      `Predictor` class to instantiate.
  **kwargs
      Keyword arguments to pass to the predictor constructor.
class_docstr_tok: ["An","`","Estimator","`","that","uses","an","external","fitting","mechanism",",","thus","eliminating","the","need","for","a","Trainer",".","Differs","from","DummyEstimator","in","that","DummyEstimator","does","not","use","the","training","data",",","but","merely","trains","at","prediction","time",".","Parameters","--","--","--","--","--","predictor_cls","`","Predictor","`","class","to","instantiate",".","*","*","kwargs","Keyword","arguments","to","pass","to","the","predictor","constructor","."]
code: def train(
      self, training_data: Dataset, validation_dataset=None
  ) -> Predictor:
      return self.predictor.train(training_data)
  
file_path: gluonts/src/gluonts/ext/rotbaum/_estimator.py
imports: from gluonts.core.component import validated
  from gluonts.dataset.common import Dataset
  from gluonts.model.estimator import Estimator, Predictor
  from ._predictor import TreePredictor
label_desc: Use image node_id 2 for calling the ThirdPartyEstimator obj's underlying member method code with example usage: obj.train(training_data, validation_dataset) and returns: self