code: def main(config, profiler_options):
      from models import build_model, build_loss
      from data_loader import get_dataloader
      from trainer import Trainer
      from post_processing import get_post_processing
      from utils import get_metric
  
      if paddle.device.cuda.device_count() > 1:
          dist.init_parallel_env()
          config["distributed"] = True
      else:
          config["distributed"] = False
      train_loader = get_dataloader(
          config["dataset"]["train"], config["distributed"]
      )
      assert train_loader is not None
      if "validate" in config["dataset"]:
          validate_loader = get_dataloader(
              config["dataset"]["validate"], False
          )
      else:
          validate_loader = None
      criterion = build_loss(config["loss"])
      config["arch"]["backbone"]["in_channels"] = (
          3
          if config["dataset"]["train"]["dataset"]["args"]["img_mode"]
          != "GRAY"
          else 1
      )
      model = build_model(config["arch"])
      # set @to_static for benchmark, skip this by default.
      post_p = get_post_processing(config["post_processing"])
      metric = get_metric(config["metric"])
      trainer = Trainer(
          config=config,
          model=model,
          criterion=criterion,
          train_loader=train_loader,
          post_process=post_p,
          metric_cls=metric,
          validate_loader=validate_loader,
          profiler_options=profiler_options,
      )
      trainer.train()
  
file_path: PaddleOCR/benchmark/PaddleOCR_DBNet/tools/train.py
imports: import os
  import sys
  import pathlib
  import paddle
  import paddle.distributed
  from utils import Config, ArgsParser
label_desc: Use image node_id 2 for calling a global function with example usage: main(config, profiler_options) without return types