code: def run_eval(
      file_root, annotations_file, weights_file, batch_size, steps
  ):
      model = YOLOv4Model()
      model.load_weights(weights_file)
  
      seed = int.from_bytes(os.urandom(4), "little")
  
      pipeline = YOLOv4Pipeline(
          file_root,
          annotations_file,
          batch_size,
          (608, 608),
          1,
          0,
          seed,
          dali_use_gpu=True,
          is_training=False,
      )
      dataset = pipeline.dataset()
  
      model.compile(run_eagerly=True)
      model.evaluate(pipeline.dataset(), steps=steps)
  
file_path: DALI/docs/examples/use_cases/tensorflow/yolov4/src/main.py
imports: from model import YOLOv4Model
  from dali.pipeline import YOLOv4Pipeline
  from img import read_img, draw_img, save_img, add_bboxes
  import inference
  import train
  import os
label_desc: Use image node_id 3 for calling a global function with example usage: run_eval(file_root, annotations_file, weights_file, batch_size, steps) without return types