text: import os
  import inspect
  import sys
  
  import torch
  from torch.utils.tensorboard import SummaryWriter
  
  current_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
  parent_dir = os.path.dirname(current_dir)
  sys.path.insert(0, parent_dir)
  
  from models.UNet import UNet
  from models.ResUNet import ResUNet
  from models.PreactivationResUNet import PreactResUNet
  from models.CENet import CE_Net
  from models.Segnet import SegNet
  from models.AttentionUnet import AttU_Net
  from models.FCN import FCN_ResNet101
  from models.Unet_nested import UNet_Nested
  from models.DeepLabV3 import Deeplabv3_ResNet101
  from models.PSPNet import PSPNet
  
  models = [
      "unet",
      "resunet",
      "proposed_cnn",
      "cenet",
      "segnet",
      "nested_unet",
      "attention_unet",
      "fcn_resnet101",
      "deeplabv3_resnet101",
      "pspnet",
  ]
  
  for m in models:
      if m == "unet":
          model = UNet(in_channels=1, num_classes=3, init_features=32)
      elif m == "resunet":
          model = ResUNet(in_channels=1, num_classes=3, init_features=32)
      elif m == "proposed_cnn":
          model = PreactResUNet(in_channels=1, num_classes=3, init_features=32)
      elif m == "cenet":
          model = CE_Net(in_channels=1, num_classes=3)
      elif m == "segnet":
          model = SegNet(in_channels=1, num_classes=3)
      elif m == "nested_unet":
          model = UNet_Nested(in_channels=1, num_classes=3)
      elif m == "attention_unet":
          model = AttU_Net(in_channels=1, num_classes=3)
      elif m == "fcn_resnet101":
          model = FCN_ResNet101(in_channels=1, num_classes=3, pretrained=False)
      elif m == "deeplabv3_resnet101":
          model = Deeplabv3_ResNet101(in_channels=1, num_classes=3, pretrained=False)
      elif m == "pspnet":
          model = PSPNet(num_classes=3, pretrained=False, backend="resnet101")
  
      writer = SummaryWriter(f"logs/graphs/{m}")
  
      data = torch.zeros([2, 1, 480, 640])
      writer.add_graph(model, data)
      writer.close()
  