code: def setup(dataset, model, pretrained_model, batchsize, input_size):
      dataset_name = dataset
      if dataset_name == "cityscapes":
          dataset = CityscapesSemanticSegmentationDataset(
              split="val", label_resolution="fine"
          )
          label_names = cityscapes_semantic_segmentation_label_names
      elif dataset_name == "ade20k":
          dataset = ADE20KSemanticSegmentationDataset(split="val")
          label_names = ade20k_semantic_segmentation_label_names
      elif dataset_name == "camvid":
          dataset = CamVidDataset(split="test")
          label_names = camvid_label_names
      elif dataset_name == "voc":
          dataset = VOCSemanticSegmentationDataset(split="val")
          label_names = voc_semantic_segmentation_label_names
  
      def eval_(out_values, rest_values):
          (pred_labels,) = out_values
          (gt_labels,) = rest_values
  
          result = eval_semantic_segmentation(pred_labels, gt_labels)
  
          for iu, label_name in zip(result["iou"], label_names):
              print("{:>23} : {:.4f}".format(label_name, iu))
          print("=" * 34)
          print("{:>23} : {:.4f}".format("mean IoU", result["miou"]))
          print(
              "{:>23} : {:.4f}".format(
                  "Class average accuracy",
                  result["mean_class_accuracy"],
              )
          )
          print(
              "{:>23} : {:.4f}".format(
                  "Global average accuracy", result["pixel_accuracy"]
              )
          )
  
      cls, pretrained_models, default_batchsize = models[model]
      if pretrained_model is None:
          pretrained_model = pretrained_models.get(
              dataset_name, dataset_name
          )
      if input_size is None:
          input_size = None
      else:
          input_size = (input_size, input_size)
  
      kwargs = {
          "n_class": len(label_names),
          "pretrained_model": pretrained_model,
      }
      if model in ["pspnet_resnet50", "pspnet_resnet101"]:
          kwargs.update({"input_size": input_size})
      elif model == "deeplab_v3plus_xception65":
          kwargs.update({"min_input_size": input_size})
      model = cls(**kwargs)
  
      if batchsize is None:
          batchsize = default_batchsize
  
      return dataset, eval_, model, batchsize
  
imports: import argparse
  import chainer
  from chainer import iterators
  from chainercv.datasets import ade20k_semantic_segmentation_label_names
  from chainercv.datasets import ADE20KSemanticSegmentationDataset
  from chainercv.datasets import camvid_label_names
  from chainercv.datasets import CamVidDataset
  from chainercv.datasets import cityscapes_semantic_segmentation_label_names
  from chainercv.datasets import CityscapesSemanticSegmentationDataset
  from chainercv.datasets import voc_semantic_segmentation_label_names
  from chainercv.datasets import VOCSemanticSegmentationDataset
  from chainercv.evaluations import eval_semantic_segmentation
  from chainercv.experimental.links import PSPNetResNet101
  from chainercv.experimental.links import PSPNetResNet50
  from chainercv.links import DeepLabV3plusXception65
  from chainercv.links import SegNetBasic
  from chainercv.utils import apply_to_iterator
  from chainercv.utils import ProgressHook
label_desc: Use image node_id 1 for calling a global function with example usage: setup(dataset, model, pretrained_model, batchsize, input_size) and returns: dataset,  eval_,  model,  batchsize