code: def run_interaction(self, train, compose):
      data = [
          {
              "image": np.ones((1, 2, 2, 2)).astype(np.float32),
              "label": np.ones((1, 2, 2, 2)),
          }
          for _ in range(5)
      ]
      network = torch.nn.Linear(2, 2)
      lr = 1e-3
      opt = torch.optim.SGD(network.parameters(), lr)
      loss = torch.nn.L1Loss()
      train_transforms = Compose(
          [
              FindAllValidSlicesd(label="label", sids="sids"),
              AddInitialSeedPointd(
                  label="label", guidance="guidance", sids="sids"
              ),
              AddGuidanceSignald(image="image", guidance="guidance"),
              ToTensord(keys=("image", "label")),
          ]
      )
      dataset = Dataset(data, transform=train_transforms)
      data_loader = torch.utils.data.DataLoader(dataset, batch_size=5)
  
      iteration_transforms = [
          Activationsd(keys="pred", sigmoid=True),
          ToNumpyd(keys=["image", "label", "pred"]),
          FindDiscrepancyRegionsd(
              label="label", pred="pred", discrepancy="discrepancy"
          ),
          AddRandomGuidanced(
              guidance="guidance",
              discrepancy="discrepancy",
              probability="probability",
          ),
          AddGuidanceSignald(image="image", guidance="guidance"),
          ToTensord(keys=("image", "label")),
      ]
      iteration_transforms = (
          Compose(iteration_transforms)
          if compose
          else iteration_transforms
      )
  
      i = Interaction(
          transforms=iteration_transforms,
          train=train,
          max_interactions=5,
      )
      self.assertEqual(
          len(i.transforms.transforms),
          6,
          "Mismatch in expected transforms",
      )
  
      # set up engine
      engine = SupervisedTrainer(
          device=torch.device("cpu"),
          max_epochs=1,
          train_data_loader=data_loader,
          network=network,
          optimizer=opt,
          loss_function=loss,
          iteration_update=i,
      )
      engine.add_event_handler(
          IterationEvents.INNER_ITERATION_STARTED, add_one
      )
      engine.add_event_handler(
          IterationEvents.INNER_ITERATION_COMPLETED, add_one
      )
  
      engine.run()
      self.assertIsNotNone(
          engine.state.batch[0].get("guidance"), "guidance is missing"
      )
      self.assertEqual(engine.state.best_metric, 9)
  
file_path: MONAI/tests/test_deepgrow_interaction.py
imports: from __future__ import annotations
  import unittest
  import numpy
  import torch
  from monai.apps.deepgrow.interaction import Interaction
  from monai.apps.deepgrow.transforms import AddGuidanceSignald, AddInitialSeedPointd, AddRandomGuidanced, FindAllValidSlicesd, FindDiscrepancyRegionsd
  from monai.data import Dataset
  from monai.engines import SupervisedTrainer
  from monai.engines.utils import IterationEvents
  from monai.transforms import Activationsd, Compose, ToNumpyd, ToTensord
label_desc: Use image node_id 1 for calling the TestInteractions obj's underlying member method code with example usage: obj.run_interaction(train, compose) without return types