code: def on_train_epoch_end(self, state: State) -> None:
      # compute and log the metric at the end of the epoch
      step_count = self.train_progress.num_steps_completed
      acc = self.train_accuracy.compute()
      self.tb_logger.log("accuracy_epoch", acc, step_count)
  
      # reset the metric at the end of every epoch
      self.train_accuracy.reset()
  
      # step the learning rate scheduler
      self.lr_scheduler.step()
  
imports: import argparse
  import logging
  import sys
  import tempfile
  from argparse import Namespace
  from typing import List, Tuple
  import torch
  import torch.nn
  from torch.utils.data.dataset import Dataset, TensorDataset
  from torcheval.metrics import BinaryAccuracy
  from torchtnt.framework.state import State
  from torchtnt.framework.train import train
  from torchtnt.framework.unit import TrainUnit
  from torchtnt.utils import copy_data_to_device, init_from_env, seed, TLRScheduler
  from torchtnt.utils.loggers import TensorBoardLogger
label_desc: Use image node_id 3 for calling the MyTrainUnit obj's underlying member method code with example usage: obj.on_train_epoch_end(state) without return types