docstr: Computes average validation accuracy
docstr_tok: ["Computes","average","validation","accuracy"]
code: def on_validation_epoch_end(self):
      """
      Computes average validation accuracy
      """
      avg_loss = torch.stack(self.val_outputs).mean()
      self.log("val_loss", avg_loss, sync_dist=True)
      self.val_outputs.clear()
  
imports: import math
  import os
  import lightning
  import pandas
  import torch
  import torch.nn.functional
  import torchtext.datasets
  from lightning.pytorch.callbacks import EarlyStopping, LearningRateMonitor, ModelCheckpoint
  from lightning.pytorch.cli import LightningCLI
  from sklearn.datasets import fetch_20newsgroups
  from sklearn.metrics import accuracy_score
  from sklearn.model_selection import train_test_split
  from torch import nn
  from torch.utils.data import DataLoader
  from torch.utils.data.dataset import random_split
  from torchdata.datapipes.iter import IterDataPipe
  from torchtext.data.functional import to_map_style_dataset
  from torchtext.datasets import AG_NEWS
  from transformers import BertModel, BertTokenizer
  import mlflow.pytorch
label_desc: Use image node_id 6 for calling the BertNewsClassifier obj's underlying member method code with example usage: obj.on_validation_epoch_end() without return types