code: def train_qa_s2s_epoch(
      model,
      dataset,
      tokenizer,
      optimizer,
      scheduler,
      args,
      e=0,
      curriculum=False,
  ):
      model.train()
      # make iterator
      if curriculum:
          train_sampler = SequentialSampler(dataset)
      else:
          train_sampler = RandomSampler(dataset)
      model_collate_fn = functools.partial(
          make_qa_s2s_batch,
          tokenizer=tokenizer,
          max_len=args.max_length,
          device="cuda:0",
      )
      data_loader = DataLoader(
          dataset,
          batch_size=args.batch_size,
          sampler=train_sampler,
          collate_fn=model_collate_fn,
      )
      epoch_iterator = tqdm(data_loader, desc="Iteration", disable=True)
      # accumulate loss since last print
      loc_steps = 0
      loc_loss = 0.0
      st_time = time()
      for step, batch_inputs in enumerate(epoch_iterator):
          pre_loss = model(**batch_inputs)[0]
          loss = pre_loss.sum() / pre_loss.shape[0]
          loss.backward()
          # optimizer
          if step % args.backward_freq == 0:
              optimizer.step()
              scheduler.step()
              model.zero_grad()
          # some printing within the epoch
          loc_loss += loss.item()
          loc_steps += 1
          if step % args.print_freq == 0 or step == 1:
              print(
                  "{:2d} {:5d} of {:5d} \t L: {:.3f} \t -- {:.3f}".format(
                      e,
                      step,
                      len(dataset) // args.batch_size,
                      loc_loss / loc_steps,
                      time() - st_time,
                  )
              )
              loc_loss = 0
              loc_steps = 0
  
imports: import functools
  import math
  import os
  from random import choice, randint
  from time import time
  import datasets
  import faiss
  import numpy
  import pandas
  import torch
  import torch.utils.checkpoint
  from elasticsearch import Elasticsearch
  from elasticsearch.helpers import bulk, streaming_bulk
  from torch import nn
  from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler
  from tqdm import tqdm
  from transformers import AdamW, AutoModel, AutoModelForSeq2SeqLM, AutoTokenizer, get_linear_schedule_with_warmup
label_desc: Use image node_id 11 for calling a global function with example usage: train_qa_s2s_epoch(model, dataset, tokenizer, optimizer, scheduler, args, e, curriculum) without return types