code: def write_region_metrics(self, epoch):
      if len(self.metric_data) > 1:
          metric_sum = 0
          for region in self.metric_data:
              metric = self.metric_data[region].mean()
              self.logger.info(
                  f"{self.tag_name} => Epoch[{epoch}] Metrics (Dice) -- Region: {region:0>2d}: {metric:.4f}"
              )
  
              self.writer.add_scalar(
                  f"{self.tag_name}_dice_{region:0>2d}", metric, epoch
              )
              metric_sum += metric
  
          metric_avg = metric_sum / len(self.metric_data)
          self.writer.add_scalar(
              f"{self.tag_name}_dice_regions_avg", metric_avg, epoch
          )
  
      self.writer.flush()
      self.metric_data = {}
  
imports: import logging
  import math
  import statistics
  from typing import TYPE_CHECKING, Any, Callable, Dict, Optional
  import numpy
  import torch
  import torch.distributed
  from monai.config import IgniteInfo
  from monai.metrics import compute_dice
  from monai.utils import min_version, optional_import
label_desc: Use image node_id 5 for calling the TensorBoardImageHandler obj's underlying member method code with example usage: obj.write_region_metrics(epoch) without return types