code: def eval_unet3d():
      # UNet3D
      from extra.models.unet3d import UNet3D
      from extra.datasets.kits19 import (
          iterate,
          sliding_window_inference,
      )
      from examples.mlperf.metrics import get_dice_score
  
      mdl = UNet3D()
      mdl.load_from_pretrained()
      s = 0
      st = time.perf_counter()
      for i, (image, label) in enumerate(iterate(), start=1):
          mt = time.perf_counter()
          pred, label = sliding_window_inference(mdl, image, label)
          et = time.perf_counter()
          print(
              f"{(mt-st)*1000:.2f} ms loading data, {(et-mt)*1000:.2f} ms to run model"
          )
          s += get_dice_score(pred, label).mean()
          print(f"****** {s:.2f}/{i}  {s/i:.5f} Mean DICE score")
          st = time.perf_counter()
  
file_path: tinygrad/examples/mlperf/model_eval.py
imports: import time
  from pathlib import Path
  import numpy
  from tinygrad import Tensor, Device, dtypes, GlobalCounters
  from tinygrad.jit import TinyJit
  from tinygrad.nn.state import get_parameters, load_state_dict, safe_load
  from tinygrad.helpers import getenv, Timing
  from examples.mlperf import helpers
label_desc: Use image node_id 3 for calling a global function with example usage: eval_unet3d() without return types