class_docstr: PyTorch Lightning module for the meta models.
class_docstr_tok: ["PyTorch","Lightning","module","for","the","meta","models","."]
code: def validation_epoch_end(self, validation_step_outputs):
      self.log("val_crps_macro", self.val_crps_macro, prog_bar=True)
      self.log("val_loss_macro", self.val_loss_macro, prog_bar=True)
      if self.val_crps_scaled:
          self.log(
              "val_crps_scaled_macro",
              self.val_crps_scaled_macro,
              prog_bar=True,
          )
  
file_path: gluonts/src/gluonts/nursery/few_shot_prediction/src/meta/models/module.py
imports: from typing import Any, Dict, Optional, List
  import pytorch_lightning
  import torch
  import torch.optim
  from torch import nn
  from torchmetrics import Metric
  from torch.optim.lr_scheduler import ReduceLROnPlateau
  from meta.data.batch import TripletBatch
  from meta.models.model import MetaModel
  from meta.common.torch import get_mask
label_desc: Use image node_id 5 for calling the MetaLightningModule obj's underlying member method code with example usage: obj.validation_epoch_end(validation_step_outputs) without return types