code: def validate_coot(
      config,
      model,
      val_loader,
      epoch,
      constrastive_loss,
      cmc_loss,
      writer,
      logger,
      use_cuda=True,
  ):
      """Validate COOT model
  
      Args:
          model: COOT model
          dataloader
          epoch: current epoch number
          constrastive_loss: MaxMargingRanking loss
          cmc_loss: Cross-modal cycle-consistensy loss
          writer: tensorboard writer
          logger
          use_cuda (bool): use GPU
  
      Returns:
          Retrieval performance
      """
  
      model.eval()
      max_step = len(val_loader)
  
      # collect embeddings
      vid_emb_list = []
      par_emb_list = []
      clip_emb_list = []
      sent_emb_list = []
      for step, data_dict in enumerate(val_loader):
          (
              vid_id,
              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)
          if step == 0:
              print(f"ids {vid_id[:4]}...")
          # 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,
          )
  
          # collect embeddings
          vid_emb_list.extend(vid_emb.detach().cpu())
          par_emb_list.extend(par_emb.detach().cpu())
  
          # clip-sentence embeddings
          clip_emb_list.extend(clip_emb.detach().cpu())
          sent_emb_list.extend(sent_emb.detach().cpu())
  
          # logging
          if step % 10 == 0:
              logger.info(
                  f"Val [{step}/{max_step}] Loss {loss.item():.4f}"
              )
      vid_emb_list = torch.stack(vid_emb_list, 0)
      par_emb_list = torch.stack(par_emb_list, 0)
      clip_emb_list = torch.stack(clip_emb_list, 0)
      sent_emb_list = torch.stack(sent_emb_list, 0)
      # video text retrieval
      vid_emb_list = F.normalize(vid_emb_list).numpy()
      par_emb_list = F.normalize(par_emb_list).numpy()
  
      v2p_res, _ = coot_utils.compute_retr_vid_to_par(
          vid_emb_list, par_emb_list
      )
      p2v_res, _ = coot_utils.compute_retr_par_to_vid(
          vid_emb_list, par_emb_list
      )
      sum_at_1 = v2p_res["r1"] + p2v_res["r1"]
  
      logger.info(coot_utils.EVALHEADER)
      logger.info(
          coot_utils.retrieval_results_to_str(p2v_res, "Par2Vid")
      )
      logger.info(
          coot_utils.retrieval_results_to_str(v2p_res, "Vid2Par")
      )
  
      # clip sentence retrieval
      clip_emb_list = F.normalize(clip_emb_list).numpy()
      sent_emb_list = F.normalize(sent_emb_list).numpy()
  
      c2s_res, _ = coot_utils.compute_retr_vid_to_par(
          clip_emb_list, sent_emb_list
      )
      s2c_res, _ = coot_utils.compute_retr_par_to_vid(
          clip_emb_list, sent_emb_list
      )
      c2s_sum_at_1 = c2s_res["r1"] + s2c_res["r1"]
      logger.info(coot_utils.EVALHEADER)
      logger.info(
          coot_utils.retrieval_results_to_str(s2c_res, "Sen2Shot")
      )
      logger.info(
          coot_utils.retrieval_results_to_str(c2s_res, "Shot2Sen")
      )
  
      writer.add_scalar("val_loss_epoch", loss, epoch)
      writer.add_scalar(
          "val_R1_Sentence2Clip_epoch", s2c_res["r1"], epoch
      )
      writer.add_scalar(
          "val_R5_Sentence2Clip_acc_epoch", s2c_res["r5"], epoch
      )
      writer.add_scalar(
          "val_R10_Sentence2Clip_acc_epoch", s2c_res["r10"], epoch
      )
  
      writer.add_scalar("val_loss_epoch", loss, epoch)
      writer.add_scalar(
          "val_R1_Clip2Sentence_epoch", c2s_res["r1"], epoch
      )
      writer.add_scalar(
          "val_R5_Clip2Sentence_acc_epoch", c2s_res["r5"], epoch
      )
      writer.add_scalar(
          "val_R10_Clip2Sentence_acc_epoch", c2s_res["r10"], epoch
      )
  
      return (
          (v2p_res, p2v_res, sum_at_1),
          (c2s_res, s2c_res, c2s_sum_at_1),
      )
  
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 2 for calling a global function with example usage: validate_coot(config, model, val_loader, epoch, constrastive_loss, cmc_loss, writer, logger, use_cuda) and returns: v2p_res,  p2v_res,  sum_at_1,  c2s_res,  s2c_res,  c2s_sum_at_1