code: def on_validation_epoch_end(
      self,
      epoch_no: int,
      epoch_loss: float,
      training_network,
      trainer,
  ) -> bool:
      super().on_validation_epoch_end(
          epoch_no, epoch_loss, training_network, trainer
      )
  
      if len(self.validation_loss_history) > 1:
          if (
              self.validation_loss_history[-1]
              > self.validation_loss_history[-2]
          ):
              self.counter += 1
          else:
              self.counter = 0  # reset if not successive
  
      if self.counter >= self.patience:
          return False
      else:
          return True
  
imports: import importlib
  from copy import deepcopy
  from typing import Dict, Union, Optional, List
  import numpy
  import pandas
  import mxnet
  from sklearn.preprocessing import OrdinalEncoder
  from gluonts.dataset.pandas import PandasDataset
  from gluonts.mx import DeepAREstimator, Trainer
  from gluonts.mx.trainer.callback import TrainingHistory
  from gluonts.mx.distribution.student_t import StudentTOutput
  from lightwood.helpers.log import log
  from lightwood.mixer.base import BaseMixer
  from lightwood.api.types import PredictionArguments
  from lightwood.data.encoded_ds import EncodedDs, ConcatedEncodedDs
label_desc: Use image node_id 2 for calling the EarlyStop obj's underlying member method code with example usage: obj.on_validation_epoch_end(epoch_no, epoch_loss, training_network, trainer) and returns: False, True