code: def train_coot(
      config,
      base_iter,
      model,
      dataloader,
      epoch,
      constrastive_loss,
      cmc_loss,
      optimizer,
      writer,
      logger,
      use_cuda=True,
  ):
      """Train COOT model
  
      Args:
          base_iter: training iteration counter
          model: COOT model
          dataloader
          epoch: current epoch number
          constrastive_loss: MaxMargingRanking loss
          cmc_loss: Cross-modal cycle-consistensy loss
          optimizer
          writer: tensorboard writer
          logger
          use_cuda (bool): use GPU
  
      Returns:
          base_itr: iteration counter after current epoch
      """
  
      max_step = len(dataloader)
      timer_start_train = timer()
      model.train()
      # train one epoch
      logger.info(
          "---------- Training epoch {} ----------".format(epoch)
      )
      for step, data_dict in enumerate(dataloader):
          base_iter = base_iter + 1
          (
              _,
              vid_frames,
              vid_frames_mask,
              vid_frames_len,
              par_cap_vectors,
              par_cap_mask,
              par_cap_len,
              clip_num,
              clip_frames,
              clip_frames_len,
              clip_frames_mask,
              sent_num,
              sent_cap_vectors,
              sent_cap_mask,
              sent_cap_len,
          ) = unpack_data(data_dict, use_cuda)
  
          # forward pass
          (
              vid_emb,
              clip_emb,
              vid_context,
              clip_emb_reshape,
              clip_emb_mask,
              clip_emb_lens,
          ) = model.encode_video(
              vid_frames,
              vid_frames_mask,
              vid_frames_len,
              clip_num,
              clip_frames,
              clip_frames_len,
              clip_frames_mask,
          )
          (
              par_emb,
              sent_emb,
              par_context,
              sent_emb_reshape,
              sent_emb_mask,
              sent_emb_lens,
          ) = model.encode_paragraph(
              par_cap_vectors,
              par_cap_mask,
              par_cap_len,
              sent_num,
              sent_cap_vectors,
              sent_cap_mask,
              sent_cap_len,
          )
          loss = compute_constrastive_loss(
              config,
              constrastive_loss,
              vid_emb,
              par_emb,
              clip_emb,
              sent_emb,
              vid_context,
              par_context,
          )
          loss += compute_cmc_loss(
              cmc_loss,
              config.CONFIG.TRAIN.LOSS_CYCLE_CONS_W,
              clip_emb_reshape,
              clip_emb_mask,
              clip_emb_lens,
              sent_emb_reshape,
              sent_emb_mask,
              sent_emb_lens,
          )
  
          # backward pass
          optimizer.zero_grad()
          loss.backward()
          optimizer.step()
  
          # logging
          if step % 10 == 0:
              el_time = (timer() - timer_start_train) / 60
              l_ms = len(str(max_step))
              str_step = ("{:" + str(l_ms) + "d}").format(step)
              print_string = (
                  f"E{epoch}[{str_step}/{max_step}] T {el_time:.3f}m "
                  f"LR {optimizer.param_groups[0]['lr']:5.3e} "
                  f"L {loss:.4f}"
              )
              logger.info(print_string)
  
      time_total = timer() - timer_start_train
      logger.info(
          "Training epoch {} took {:.3f}s ".format(epoch, time_total)
      )
  
      writer.add_scalar("train_loss_iteration", loss, base_iter)
      writer.add_scalar(
          "train_batch_size_iteration", vid_frames.size(0), base_iter
      )
      writer.add_scalar(
          "learning_rate", optimizer.param_groups[0]["lr"], base_iter
      )
  
      return base_iter
  
imports: from timeit import default_timer
  import torch
  from torch.nn import functional
  from ..None import coot_utils
  from ..coot_utils import compute_constrastive_loss, compute_cmc_loss
  from ..coot_utils import unpack_data
label_desc: Use image node_id 1 for calling a global function with example usage: train_coot(config, base_iter, model, dataloader, epoch, constrastive_loss, cmc_loss, optimizer, writer, logger, use_cuda) and returns: base_iter