class_docstr: An abstract class representing a trainable model.
  
  The underlying model is trained by calling the `train` method with a
  training `Dataset`, producing a `Predictor` object.
docstr: Train the estimator on the given data.
  
  Parameters
  ----------
  training_data
      Dataset to train the model on.
  validation_data
      Dataset to validate the model on during training.
  
  Returns
  -------
  Predictor
      The predictor containing the trained model.
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code: def train(
      self,
      training_data: Dataset,
      validation_data: Optional[Dataset] = None,
  ) -> Predictor:
      """
      Train the estimator on the given data.
  
      Parameters
      ----------
      training_data
          Dataset to train the model on.
      validation_data
          Dataset to validate the model on during training.
  
      Returns
      -------
      Predictor
          The predictor containing the trained model.
      """
      raise NotImplementedError
  
file_path: gluonts/src/gluonts/model/estimator.py
imports: from typing import Optional
  from typing_extensions import Protocol, runtime_checkable
  import gluonts
  from gluonts.core.component import from_hyperparameters
  from gluonts.dataset.common import Dataset
  from gluonts.model.predictor import Predictor
label_desc: Use image node_id 2 for calling the Estimator obj's underlying member method code with example usage: obj.train(training_data, validation_data) without return types