code: def train(model, config, loader, optimizer, writer, epoch):
      model.train()
      loss_accum = 0
  
      for step, (batch_dict, labels, others) in enumerate(loader):
          feed_dict = data2tensor(batch_dict)
          labels = paddle.to_tensor(labels)
  
          pred = paddle.reshape(
              model(feed_dict),
              shape=[
                  -1,
              ],
          )
          loss = reg_criterion(pred, labels)
          loss.backward()
          optimizer.step()
          optimizer.clear_grad()
  
          loss_accum += loss.numpy()
  
          if step % 100 == 0:
              log.info(
                  "Epoch: %s | Step: %s | Train loss: %.6f"
                  % (epoch, step + 1, loss_accum / (step + 1))
              )
  
      return loss_accum / (step + 1)
  
imports: import os
  import sys
  import time
  import shutil
  import random
  import argparse
  import numpy
  from tqdm import tqdm
  from functools import reduce
  from datetime import datetime
  from tensorboardX import SummaryWriter
  import paddle
  import paddle.nn
  import paddle.nn.functional
  import paddle.distributed
  import pgl
  from pgl.utils.data import Dataloader
  from pgl.utils.logger import log
  from ogb.lsc import PCQM4MEvaluator
  from ogb.utils import smiles2graph
  from utils.config import prepare_config, make_dir
  from utils.logger import prepare_logger, log_to_file
  import model
  import dataset
label_desc: Use image node_id 2 for calling a global function with example usage: train(model, config, loader, optimizer, writer, epoch) and returns: unknown