code: def run(
      model,
      optimizer,
      scheduler,
      train_loader,
      val_loaders,
      logger,
      writer,
      scaler,
      args,
  ):
      device = torch.device(args.device)
      # wrap the loader in a logger
      loader = iter(logger.log_every(train_loader))
      # output channels
      model_out_channels = (
          model.module.output_channels
          if args.distributed
          else model.output_channels
      )
  
      torch.set_num_threads(args.threads)
  
      sequence_criterion = utils.SequenceLoss(
          gamma=args.gamma,
          max_flow=args.max_disparity,
          exclude_large_flows=args.flow_loss_exclude_large,
      ).to(device)
  
      if args.consistency_weight:
          consistency_criterion = utils.FlowSequenceConsistencyLoss(
              args.gamma,
              resize_factor=0.25,
              rescale_factor=0.25,
              rescale_mode="bilinear",
          ).to(device)
      else:
          consistency_criterion = None
  
      if args.psnr_weight:
          psnr_criterion = utils.PSNRLoss().to(device)
      else:
          psnr_criterion = None
  
      if args.smoothness_weight:
          smoothness_criterion = utils.SmoothnessLoss().to(device)
      else:
          smoothness_criterion = None
  
      if args.photometric_weight:
          photometric_criterion = utils.FlowPhotoMetricLoss(
              ssim_weight=args.photometric_ssim_weight,
              max_displacement_ratio=args.photometric_max_displacement_ratio,
              ssim_use_padding=False,
          ).to(device)
      else:
          photometric_criterion = None
  
      for step in range(args.start_step + 1, args.total_iterations + 1):
          data_blob = next(loader)
          optimizer.zero_grad()
  
          # unpack the data blob
          image_left, image_right, disp_mask, valid_disp_mask = (
              x.to(device) for x in data_blob
          )
          with torch.cuda.amp.autocast(
              enabled=args.mixed_precision, dtype=torch.float16
          ):
              disp_predictions = model(
                  image_left,
                  image_right,
                  flow_init=None,
                  num_iters=args.recurrent_updates,
              )
              # different models have different outputs, make sure we get the right ones for this task
              disp_predictions = make_stereo_flow(
                  disp_predictions, model_out_channels
              )
              # should the architecture or training loop require it, we have to adjust the disparity mask
              # target to possibly look like an optical flow mask
              disp_mask = make_stereo_flow(
                  disp_mask, model_out_channels
              )
              # sequence loss on top of the model outputs
  
          loss = (
              sequence_criterion(
                  disp_predictions, disp_mask, valid_disp_mask
              )
              * args.flow_loss_weight
          )
  
          if args.consistency_weight > 0:
              loss_consistency = consistency_criterion(disp_predictions)
              loss += loss_consistency * args.consistency_weight
  
          if args.psnr_weight > 0:
              loss_psnr = 0.0
              for pred in disp_predictions:
                  # predictions might have 2 channels
                  loss_psnr += psnr_criterion(
                      pred * valid_disp_mask.unsqueeze(1),
                      disp_mask * valid_disp_mask.unsqueeze(1),
                  ).mean()  # mean the psnr loss over the batch
              loss += (
                  loss_psnr / len(disp_predictions) * args.psnr_weight
              )
  
          if args.photometric_weight > 0:
              loss_photometric = 0.0
              for pred in disp_predictions:
                  # predictions might have 1 channel, therefore we need to inpute 0s for the second channel
                  if model_out_channels == 1:
                      pred = torch.cat(
                          [pred, torch.zeros_like(pred)], dim=1
                      )
  
                  loss_photometric += photometric_criterion(
                      image_left, image_right, pred, valid_disp_mask
                  )  # photometric loss already comes out meaned over the batch
              loss += (
                  loss_photometric
                  / len(disp_predictions)
                  * args.photometric_weight
              )
  
          if args.smoothness_weight > 0:
              loss_smoothness = 0.0
              for pred in disp_predictions:
                  # predictions might have 2 channels
                  loss_smoothness += smoothness_criterion(
                      image_left, pred[:, :1, :, :]
                  ).mean()  # mean the smoothness loss over the batch
              loss += (
                  loss_smoothness
                  / len(disp_predictions)
                  * args.smoothness_weight
              )
  
          with torch.no_grad():
              metrics, _ = utils.compute_metrics(
                  disp_predictions[-1][
                      :, :1, :, :
                  ],  # predictions might have 2 channels
                  disp_mask[:, :1, :, :],  # so does the ground truth
                  valid_disp_mask,
                  args.metrics,
              )
  
          metrics.pop("fl-all", None)
          logger.update(loss=loss, **metrics)
  
          if scaler is not None:
              scaler.scale(loss).backward()
              scaler.unscale_(optimizer)
              if args.clip_grad_norm:
                  torch.nn.utils.clip_grad_norm_(
                      model.parameters(), max_norm=args.clip_grad_norm
                  )
              scaler.step(optimizer)
              scaler.update()
          else:
              loss.backward()
              if args.clip_grad_norm:
                  torch.nn.utils.clip_grad_norm_(
                      model.parameters(), max_norm=args.clip_grad_norm
                  )
              optimizer.step()
  
          scheduler.step()
  
          if not dist.is_initialized() or dist.get_rank() == 0:
              if (
                  writer is not None
                  and step % args.tensorboard_log_frequency == 0
              ):
                  # log the loss and metrics to tensorboard
  
                  writer.add_scalar("loss", loss, step)
                  for name, value in logger.meters.items():
                      writer.add_scalar(name, value.avg, step)
                  # log the images to tensorboard
                  pred_grid = visualization.make_training_sample_grid(
                      image_left,
                      image_right,
                      disp_mask,
                      valid_disp_mask,
                      disp_predictions,
                  )
                  writer.add_image(
                      "predictions", pred_grid, step, dataformats="HWC"
                  )
  
                  # second thing we want to see is how relevant the iterative refinement is
                  pred_sequence_grid = (
                      visualization.make_disparity_sequence_grid(
                          disp_predictions, disp_mask
                      )
                  )
                  writer.add_image(
                      "sequence",
                      pred_sequence_grid,
                      step,
                      dataformats="HWC",
                  )
  
          if step % args.save_frequency == 0:
              if not args.distributed or args.rank == 0:
                  model_without_ddp = (
                      model.module
                      if isinstance(
                          model,
                          torch.nn.parallel.DistributedDataParallel,
                      )
                      else model
                  )
                  checkpoint = {
                      "model": model_without_ddp.state_dict(),
                      "optimizer": optimizer.state_dict(),
                      "scheduler": scheduler.state_dict(),
                      "step": step,
                      "args": args,
                  }
                  os.makedirs(args.checkpoint_dir, exist_ok=True)
                  torch.save(
                      checkpoint,
                      Path(args.checkpoint_dir)
                      / f"{args.name}_{step}.pth",
                  )
                  torch.save(
                      checkpoint,
                      Path(args.checkpoint_dir) / f"{args.name}.pth",
                  )
  
          if step % args.valid_frequency == 0:
              evaluate(model, val_loaders, args, writer, step)
              model.train()
              if args.freeze_batch_norm:
                  if isinstance(
                      model, nn.parallel.DistributedDataParallel
                  ):
                      freeze_batch_norm(model.module)
                  else:
                      freeze_batch_norm(model)
  
      # one final save at the end
      if not args.distributed or args.rank == 0:
          model_without_ddp = (
              model.module
              if isinstance(
                  model, torch.nn.parallel.DistributedDataParallel
              )
              else model
          )
          checkpoint = {
              "model": model_without_ddp.state_dict(),
              "optimizer": optimizer.state_dict(),
              "scheduler": scheduler.state_dict(),
              "step": step,
              "args": args,
          }
          os.makedirs(args.checkpoint_dir, exist_ok=True)
          torch.save(
              checkpoint,
              Path(args.checkpoint_dir) / f"{args.name}_{step}.pth",
          )
          torch.save(
              checkpoint, Path(args.checkpoint_dir) / f"{args.name}.pth"
          )
  
imports: import argparse
  import os
  import warnings
  from pathlib import Path
  from typing import List, Union
  import numpy
  import torch
  import torch.distributed
  import torchvision.models.optical_flow
  import torchvision.prototype.models.depth.stereo
  import utils
  import visualization
  from parsing import make_dataset, make_eval_transform, make_train_transform, VALID_DATASETS
  from torch import nn
  from torchvision.transforms.functional import get_dimensions, InterpolationMode, resize
  from utils.metrics import AVAILABLE_METRICS
  from utils.norm import freeze_batch_norm
label_desc: Use image node_id 9 for calling a global function with example usage: run(model, optimizer, scheduler, train_loader, val_loaders, logger, writer, scaler, args) without return types