code: def run_training(
      file_root,
      annotations,
      batch_size,
      epochs,
      steps_per_epoch,
      **kwargs,
  ):
      model = train.train(
          file_root,
          annotations,
          batch_size,
          epochs,
          steps_per_epoch,
          **kwargs,
      )
  
      output = kwargs.get("output")
      if output:
          model.save_weights(output)
  
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 2 for calling a global function with example usage: run_training(file_root, annotations, batch_size, epochs, steps_per_epoch) without return types