text: import argparse
  import collections
  
  import torch
  import numpy as np
  
  import data_loader.data_loaders as module_data
  import model.loss as module_loss
  import model.metric as module_metric
  import model.model as module_arch
  from parse_config import ConfigParser
  from trainer import Trainer
  
  
  # Fix random seeds for reproducibility
  SEED = 123
  torch.manual_seed(SEED)
  torch.backends.cudnn.deterministic = True
  torch.backends.cudnn.benchmark = False
  np.random.seed(SEED)
  
  def main(config):
      logger = config.get_logger("train")
  
      # Setup data_loader instances
      data_loader = config.init_obj("data_loader", module_data)
      valid_data_loader = data_loader.split_validation()
  
      # Build model architecture, then print to console
      model = config.init_obj("arch", module_arch)
      logger.info(model)
  
      # Get function handles of loss and metrics
      criterion = getattr(module_loss, config["loss"])
      metrics = [getattr(module_metric, met) for met in config["metrics"]]
  
      # Build optimizer, learning rate scheduler. delete every lines containing
      # lr_scheduler for disabling scheduler
      trainable_params = filter(lambda p: p.requires_grad, model.parameters())
      optimizer = config.init_obj("optimizer", torch.optim, trainable_params)
  
      lr_scheduler = config.init_obj(
          "lr_scheduler", torch.optim.lr_scheduler, optimizer)
  
      trainer = Trainer(model, criterion, metrics, optimizer, config=config,
                        data_loader=data_loader, lr_scheduler=lr_scheduler,
                        valid_data_loader=valid_data_loader)
  
      trainer.train()
  
  
  if __name__ == "__main__":
      args = argparse.ArgumentParser(description="PyTorch Template")
      args.add_argument("-c", "--config", default=None, type=str,
                        help="Config file path (default: None)")
      args.add_argument("-r", "--resume", default=None, type=str,
                        help="Path to latest checkpoint (default: None)")
      args.add_argument("-d", "--device", default=None, type=str,
                        help="Indices of GPUs to enable (default: all)")
  
      # Custom cli options to modify configuration from default values given in
      # json file.
      CustomArgs = collections.namedtuple("CustomArgs", "flags type target")
      options = [
          CustomArgs(["--lr", "--learning_rate"], type=float,
                     target="optimizer;args;lr"),
          CustomArgs(["--bs", "--batch_size"], type=int,
                     target="data_loader;args;batch_size")
      ]
      config = ConfigParser.from_args(args, options)
      main(config)
  