code: def evaluate(model, loaders, args, writer=None, step=None):
      for loader_name, loader in loaders.items():
          _evaluate(
              model,
              args,
              loader,
              iterations=args.recurrent_updates,
              padder_mode=args.padder_type,
              header=f"{loader_name} evaluation",
              batch_size=args.batch_size,
              writer=writer,
              step=step,
          )
  
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 8 for calling a global function with example usage: evaluate(model, loaders, args, writer, step) without return types