code: def __call__(self, request):
      start = time.time()
      request.update({"image_path": request.get("image")})
  
      device = name_to_device(request.get("device", "cuda"))
      request["device"] = device
  
      # Run first stage
      d1, r1, l1 = self.locate_spine(request)
      image = d1["image"]
      label = d1["pred"]
  
      # Run second stage
      d2, r2, l2 = self.locate_vertebra(request, image, label)
      centroids = r2["centroids"]
  
      # Run third stage
      result_mask, l3 = self.segment_vertebra(request, image, centroids)
  
      # Finalize the mask/result
      data = copy.deepcopy(request)
      data.update({"pred": result_mask, "image": image})
      data = run_transforms(
          data,
          [Restored(keys="pred", ref_image="image")],
          log_prefix="POST(P)",
          use_compose=False,
      )
  
      begin = time.time()
      result_file, _ = Writer(label="pred")(data)
      latency_write = round(time.time() - begin, 2)
  
      total_latency = round(time.time() - start, 2)
      result_json = {
          "label_names": self.task_seg_vertebra.labels,
          "centroids": centroids,
          "latencies": {
              "locate_spine": l1,
              "locate_vertebra": l2,
              "segment_vertebra": l3,
              "write": latency_write,
              "total": total_latency,
          },
      }
  
      logger.info(
          f"Result Mask (aggregated/pre-restore): {result_mask.shape}; total_latency: {total_latency}"
      )
      return result_file, result_json
  
imports: import copy
  import logging
  import time
  from typing import Callable, Sequence
  import torch
  from tqdm import tqdm
  from monailabel.interfaces.tasks.infer_v2 import InferTask, InferType
  from monailabel.interfaces.utils.transform import run_transforms
  from monailabel.tasks.infer.basic_infer import BasicInferTask
  from monailabel.transform.post import Restored
  from monailabel.transform.writer import Writer
  from monailabel.utils.others.generic import name_to_device
label_desc: Use image node_id 9 for calling the InferVertebraPipeline obj's underlying member method code with example usage: obj.__call__(request) and returns: result_file,  result_json