docstr: Computes average test accuracy score
  
  :param outputs: outputs after every epoch end
  
  :return: output - average test loss
docstr_tok: ["Computes","average","test","accuracy","score",":","param","outputs",":","outputs","after","every","epoch","end",":","return",":","output","-","average","test","loss"]
code: def test_epoch_end(self, outputs):
      """
      Computes average test accuracy score
  
      :param outputs: outputs after every epoch end
  
      :return: output - average test loss
      """
      avg_test_acc = torch.stack(
          [x["test_acc"] for x in outputs]
      ).mean()
      self.log("avg_test_acc", avg_test_acc, sync_dist=True)
  
imports: import logging
  import os
  from argparse import ArgumentParser
  import mlflow
  import mlflow.pytorch
  import pytorch_lightning
  import torch
  from pytorch_lightning.callbacks import LearningRateMonitor
  from pytorch_lightning.callbacks import ModelCheckpoint
  from pytorch_lightning.callbacks.early_stopping import EarlyStopping
  from torch.nn import functional
  from torch.utils.data import DataLoader
  from torch.utils.data import random_split
  from torchvision import datasets
  from torchvision import transforms
label_desc: Use image node_id 9 for calling the LightningMNISTClassifier obj's underlying member method code with example usage: obj.test_epoch_end(outputs) without return types