code: def __call__(self, engine: Engine, action) -> None:
      epoch = engine.state.epoch
      batch_data = self.batch_transform(engine.state.batch)
      output_data = self.output_transform(engine.state.output)
  
      if action == "iteration":
          for bidx in range(len(batch_data)):
              y = output_data[bidx]["label"].detach().cpu().numpy()
              y_pred = output_data[bidx]["pred"].detach().cpu().numpy()
  
              for region in range(y_pred.shape[0]):
                  if (
                      region == 0 and y_pred.shape[0] > 1
                  ):  # one-hot; background
                      continue
  
                  if self.metric_data.get(region) is None:
                      self.metric_data[region] = RegionDice()
  
                  self.metric_data[region].update(
                      y_pred=y_pred[region][np.newaxis],
                      y=y[region][np.newaxis],
                  )
          return
  
      self.write_region_metrics(epoch)
      self.write_images(batch_data, output_data, epoch)
  
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 3 for calling the TensorBoardImageHandler obj's underlying member method code with example usage: obj.__call__(engine, action) without return types