code: def validation_epoch_end(
      self,
      outputs: Optional[
          List[Tuple[EvaluationDataPage, EvaluationDataPage]]
      ],
  ):
      if self.calc_cpe:
          assert outputs is not None
          eval_data_pages_g, eval_data_pages_ng = None, None
          for edp_g, edp_ng in outputs:
              if (
                  eval_data_pages_g is None
                  and eval_data_pages_ng is None
              ):
                  eval_data_pages_g = edp_g
                  eval_data_pages_ng = edp_ng
              else:
                  # pyre-fixme[16]: `Optional` has no attribute `append`
                  eval_data_pages_g.append(edp_g)
                  eval_data_pages_ng.append(edp_ng)
          self.reporter.log(
              eval_data_pages_g=eval_data_pages_g,
              eval_data_pages_ng=eval_data_pages_ng,
          )
  
imports: import logging
  from typing import List, Optional, Tuple
  import reagent.core.types
  import torch
  import torch.nn
  import torch.nn.functional
  from reagent.core.dataclasses import field
  from reagent.core.parameters import Seq2SlateParameters
  from reagent.evaluation.evaluation_data_page import EvaluationDataPage
  from reagent.model_utils.seq2slate_utils import Seq2SlateMode
  from reagent.models.seq2slate import BaselineNet, Seq2SlateTransformerNet
  from reagent.optimizer.union import Optimizer__Union
  from reagent.training.ranking.helper import ips_clamp
  from reagent.training.reagent_lightning_module import ReAgentLightningModule
label_desc: Use image node_id 6 for calling the Seq2SlateTrainer obj's underlying member method code with example usage: obj.validation_epoch_end(outputs) without return types