code: def make_eval_loader(
      dataset_name: str, args: argparse.Namespace
  ) -> torch.utils.data.DataLoader:
      if args.weights:
          weights = torchvision.models.get_weight(args.weights)
          trans = weights.transforms()
  
          def preprocessing(
              image_left, image_right, disp, valid_disp_mask
          ):
              C_o, H_o, W_o = get_dimensions(image_left)
              image_left, image_right = trans(image_left, image_right)
  
              C_t, H_t, W_t = get_dimensions(image_left)
              scale_factor = W_t / W_o
  
              if disp is not None and not isinstance(
                  disp, torch.Tensor
              ):
                  disp = torch.from_numpy(disp)
                  if W_t != W_o:
                      disp = (
                          resize(
                              disp,
                              (H_t, W_t),
                              mode=InterpolationMode.BILINEAR,
                          )
                          * scale_factor
                      )
              if valid_disp_mask is not None and not isinstance(
                  valid_disp_mask, torch.Tensor
              ):
                  valid_disp_mask = torch.from_numpy(valid_disp_mask)
                  if W_t != W_o:
                      valid_disp_mask = resize(
                          valid_disp_mask,
                          (H_t, W_t),
                          mode=InterpolationMode.NEAREST,
                      )
              return image_left, image_right, disp, valid_disp_mask
  
      else:
          preprocessing = make_eval_transform(args)
  
      val_dataset = make_dataset(
          dataset_name, args.dataset_root, transforms=preprocessing
      )
      if args.distributed:
          sampler = torch.utils.data.distributed.DistributedSampler(
              val_dataset, shuffle=False, drop_last=False
          )
      else:
          sampler = torch.utils.data.SequentialSampler(val_dataset)
  
      val_loader = torch.utils.data.DataLoader(
          val_dataset,
          sampler=sampler,
          batch_size=args.batch_size,
          pin_memory=True,
          num_workers=args.workers,
      )
  
      return val_loader
  
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 7 for calling a global function with example usage: make_eval_loader(dataset_name, args) and returns: val_loader, image_left,  image_right,  disp,  valid_disp_mask