code: def test_compute(self, data, expected):
      # Set up handlers
      handlers = [
          # Mark with Ignite Event
          MarkHandler(Events.STARTED),
          # Mark with literal
          MarkHandler("EPOCH_STARTED"),
          # Mark with literal and providing the message
          MarkHandler("EPOCH_STARTED", "Start of the epoch"),
          # Define a range using one prefix (between BATCH_STARTED and BATCH_COMPLETED)
          RangeHandler("Batch"),
          # Define a range using a pair of events
          RangeHandler((Events.STARTED, Events.COMPLETED)),
          # Define a range using a pair of literals
          RangeHandler(
              ("GET_BATCH_STARTED", "GET_BATCH_COMPLETED"),
              msg="Batching!",
          ),
          # Define a range using a pair of literal and events
          RangeHandler(("GET_BATCH_STARTED", Events.COMPLETED)),
          # Define the start of range using literal
          RangePushHandler("ITERATION_STARTED"),
          # Define the start of range using event
          RangePushHandler(Events.ITERATION_STARTED, "Iteration 2"),
          # Define the start of range using literals and providing message
          RangePushHandler("EPOCH_STARTED", "Epoch 2"),
          # Define the end of range using Ignite Event
          RangePopHandler(Events.ITERATION_COMPLETED),
          RangePopHandler(Events.EPOCH_COMPLETED),
          # Define the end of range using literal
          RangePopHandler("ITERATION_COMPLETED"),
          # Other handlers
          StatsHandler(
              tag_name="train",
              output_transform=from_engine(["label"], first=True),
          ),
      ]
  
      # Set up an engine
      engine = SupervisedEvaluator(
          device=torch.device("cpu:0"),
          val_data_loader=data,
          epoch_length=1,
          network=torch.nn.PReLU(),
          postprocessing=lambda x: dict(pred=x["pred"] + 1.0),
          decollate=True,
          val_handlers=handlers,
      )
      # Run the engine
      engine.run()
  
      # Get the output from the engine
      output = engine.state.output[0]
  
      assert_allclose(output["pred"], expected)
  
file_path: MONAI/tests/test_handler_nvtx.py
imports: from __future__ import annotations
  import unittest
  import torch
  from ignite.engine import Events
  from parameterized import parameterized
  from monai.engines import SupervisedEvaluator
  from monai.handlers import StatsHandler, from_engine
  from monai.handlers.nvtx_handlers import MarkHandler, RangeHandler, RangePopHandler, RangePushHandler
  from monai.utils import optional_import
  from tests.utils import assert_allclose
label_desc: Use image node_id 1 for calling the TestHandlerDecollateBatch obj's underlying member method code with example usage: obj.test_compute(data, expected) without return types