code: def evaluate(model, args):
      val_datasets = args.val_dataset or []
  
      if args.weights and args.test_only:
          weights = torchvision.models.get_weight(args.weights)
          trans = weights.transforms()
  
          def preprocessing(img1, img2, flow, valid_flow_mask):
              img1, img2 = trans(img1, img2)
              if flow is not None and not isinstance(
                  flow, torch.Tensor
              ):
                  flow = torch.from_numpy(flow)
              if valid_flow_mask is not None and not isinstance(
                  valid_flow_mask, torch.Tensor
              ):
                  valid_flow_mask = torch.from_numpy(valid_flow_mask)
              return img1, img2, flow, valid_flow_mask
  
      else:
          preprocessing = OpticalFlowPresetEval()
  
      for name in val_datasets:
          if name == "kitti":
              # Kitti has different image sizes, so we need to individually pad them, we can't batch.
              # see comment in InputPadder
              if args.batch_size != 1 and (
                  not args.distributed or args.rank == 0
              ):
                  warnings.warn(
                      f"Batch-size={args.batch_size} was passed. For technical reasons, evaluating on Kitti can only be done with a batch-size of 1."
                  )
  
              val_dataset = KittiFlow(
                  root=args.dataset_root,
                  split="train",
                  transforms=preprocessing,
              )
              _evaluate(
                  model,
                  args,
                  val_dataset,
                  num_flow_updates=24,
                  padder_mode="kitti",
                  header="Kitti val",
                  batch_size=1,
              )
          elif name == "sintel":
              for pass_name in ("clean", "final"):
                  val_dataset = Sintel(
                      root=args.dataset_root,
                      split="train",
                      pass_name=pass_name,
                      transforms=preprocessing,
                  )
                  _evaluate(
                      model,
                      args,
                      val_dataset,
                      num_flow_updates=32,
                      padder_mode="sintel",
                      header=f"Sintel val {pass_name}",
                  )
          else:
              warnings.warn(
                  f"Can't validate on {val_dataset}, skipping."
              )
  
file_path: vision/references/optical_flow/train.py
imports: import argparse
  import warnings
  from math import ceil
  from pathlib import Path
  import torch
  import torchvision.models.optical_flow
  import utils
  from presets import OpticalFlowPresetEval, OpticalFlowPresetTrain
  from torchvision.datasets import FlyingChairs, FlyingThings3D, HD1K, KittiFlow, Sintel
label_desc: Use image node_id 3 for calling a global function with example usage: evaluate(model, args) and returns: img1,  img2,  flow,  valid_flow_mask