docstr: Experimental: this feature may change in future versions.
  Train the estimator, starting from a previously trained
  predictor, on the given data.
  
  Parameters
  ----------
  predictor
      A previously trained model, from which to initialize the estimator
      training.
  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_from(
      self,
      predictor: Predictor,
      training_data: Dataset,
      validation_data: Optional[Dataset] = None,
  ) -> Predictor:
      """
      Experimental: this feature may change in future versions.
      Train the estimator, starting from a previously trained
      predictor, on the given data.
  
      Parameters
      ----------
      predictor
          A previously trained model, from which to initialize the estimator
          training.
      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 1 for calling the IncrementallyTrainable obj's underlying member method code with example usage: obj.train_from(predictor, training_data, validation_data) without return types