code: def evaluate(model, loader, args, writer=None, step=None):
      os.makedirs(args.img_folder, exist_ok=True)
      checkpoint_name = (
          os.path.basename(args.checkpoint) or args.weights
      )
      image_checkpoint_folder = os.path.join(
          args.img_folder, checkpoint_name
      )
  
      metrics = {}
      base_image_folder = os.path.join(
          image_checkpoint_folder, args.dataset
      )
      os.makedirs(base_image_folder, exist_ok=True)
  
      for n_cascades in args.n_cascades:
          for n_iters in args.n_iterations:
              config = f"{n_cascades}c_{n_iters}i"
              config_image_folder = os.path.join(
                  base_image_folder, config
              )
              os.makedirs(config_image_folder, exist_ok=True)
  
              metrics[config] = _evaluate(
                  model,
                  args,
                  loader,
                  padder_mode=args.padder_type,
                  header=f"{args.dataset} evaluation@ size:{args.eval_size} n_cascades:{n_cascades} n_iters:{n_iters}",
                  batch_size=args.batch_size,
                  writer=writer,
                  step=step,
                  iterations=n_iters,
                  cascades=n_cascades,
                  save_path=config_image_folder,
                  save_images=args.save_images,
              )
  
      metric_log = []
      metric_log_dict = {}
      # print the final results
      for config in metrics:
          config_tokens = config.split("_")
          config_iters = config_tokens[1][:-1]
          config_cascades = config_tokens[0][:-1]
  
          metric_log_dict[config_cascades] = metric_log_dict.get(
              config_cascades, {}
          )
          metric_log_dict[config_cascades][config_iters] = metrics[
              config
          ]
  
          evaluation_str = f"{args.dataset} evaluation@ size:{args.eval_size} n_cascades:{config_cascades} recurrent_updates:{config_iters}"
          metrics_str = f"Metrics: {metrics[config]}"
          metric_log.extend([evaluation_str, metrics_str])
  
          print(evaluation_str)
          print(metrics_str)
  
      eval_log_name = f"{checkpoint_name.replace('.pth', '')}_eval.log"
      print("Saving eval log to: ", eval_log_name)
      with open(eval_log_name, "w") as f:
          f.write(f"Dataset: {args.dataset} @size: {args.eval_size}:\n")
          # write the dict line by line for each key, and each value in the keys
          for config_cascades in metric_log_dict:
              f.write("{\n")
              f.write(f"\t{config_cascades}: {{\n")
              for config_iters in metric_log_dict[config_cascades]:
                  # convert every metric to 4 decimal places
                  metrics = metric_log_dict[config_cascades][
                      config_iters
                  ]
                  metrics = {
                      k: float(f"{v:.3f}") for k, v in metrics.items()
                  }
                  f.write(f"\t\t{config_iters}: {metrics}\n")
              f.write("\t},\n")
              f.write("}\n")
  
file_path: vision/references/depth/stereo/cascade_evaluation.py
imports: import os
  import warnings
  import torch
  import torchvision
  import torchvision.prototype.models.depth.stereo
  import utils
  from torch.nn import functional
  from train import make_eval_loader
  from utils.metrics import AVAILABLE_METRICS
  from visualization import make_prediction_image_side_to_side
label_desc: Use image node_id 4 for calling a global function with example usage: evaluate(model, loader, args, writer, step) without return types