code: def run_batch(
      self, run_inferer_method, batched_data, batched_slices, pred
  ):
      bdata = {self.input_key: torch.as_tensor(batched_data)}
      outputs = run_inferer_method(bdata, False)
      for i, s in enumerate(batched_slices):
          p = (
              torch.sigmoid(outputs[self.output_label_key][i])
              .detach()
              .cpu()
              .numpy()
          )
          p[p > 0.5] = 1
          pred[s] = (
              LargestCCd.get_largest_cc(p)
              if self.output_largest_cc
              else p
          )
  
imports: import logging
  import os
  from typing import Callable, Sequence
  import numpy
  import torch
  from monai.apps.deepgrow.transforms import AddGuidanceFromPointsd, AddGuidanceSignald, AddInitialSeedPointd, ResizeGuidanced, RestoreLabeld, SpatialCropGuidanced
  from monai.inferers import Inferer, SimpleInferer
  from monai.transforms import AsChannelLastd, EnsureChannelFirstd, LoadImage, LoadImaged, NormalizeIntensityd, Resized, Spacingd, Transpose, Transposed
  from monailabel.interfaces.tasks.infer_v2 import InferTask, InferType
  from monailabel.tasks.infer.basic_infer import BasicInferTask
  from monailabel.transform.post import BoundingBoxd, LargestCCd
label_desc: Use image node_id 7 for calling the InferDeepgrowPipeline obj's underlying member method code with example usage: obj.run_batch(run_inferer_method, batched_data, batched_slices, pred) without return types