code: def __init__(
      self,
      config,
      args,
      model,
      emb_size=768,
      block_size=64,
      num_labels=-1,
  ):
      super().__init__()
      self.config = config
      self.bert_model = model
      self.hidden_size = config.hidden_size
      self.loss_fnt = ATLoss()
  
      self.head_extractor = nn.Linear(
          1 * config.hidden_size + args.unet_out_dim, emb_size
      )
      self.tail_extractor = nn.Linear(
          1 * config.hidden_size + args.unet_out_dim, emb_size
      )
      # self.head_extractor = nn.Linear(1 * config.hidden_size , emb_size)
      # self.tail_extractor = nn.Linear(1 * config.hidden_size , emb_size)
      self.bilinear = nn.Linear(
          emb_size * block_size, config.num_labels
      )
  
      self.emb_size = emb_size
      self.block_size = block_size
      self.num_labels = num_labels
  
      self.bertdrop = nn.Dropout(0.6)
      self.unet_in_dim = args.unet_in_dim
      self.unet_out_dim = args.unet_in_dim
      self.liner = nn.Linear(config.hidden_size, args.unet_in_dim)
      self.min_height = args.max_height
      self.channel_type = args.channel_type
      self.segmentation_net = AttentionUNet(
          input_channels=args.unet_in_dim,
          class_number=args.unet_out_dim,
          down_channel=args.down_dim,
      )
  
file_path: DeepKE/src/deepke/relation_extraction/document/model.py
imports: import torch
  import torch.nn
  from opt_einsum import contract
  from .losses import ATLoss
  import torch.nn.functional
  from .module import AttentionUNet
  from .prepro import process_long_input
label_desc: Use image node_id 1 to create a new DocREModel object from inherited base classes: nn with example: obj = DocREModel(config, args, model, emb_size, block_size, num_labels)