class_docstr: A GNN prediction head for edge-level/link-level prediction tasks.
  
  Args:
      dim_in (int): The input feature dimension.
      dim_out (int): The output feature dimension.
class_docstr_tok: ["A","GNN","prediction","head","for","edge-level\/link-level","prediction","tasks",".","Args",":","dim_in","(","int",")",":","The","input","feature","dimension",".","dim_out","(","int",")",":","The","output","feature","dimension","."]
code: def __init__(self, dim_in: int, dim_out: int):
      super().__init__()
      # Module to decode edges from node embeddings:
      if cfg.model.edge_decoding == "concat":
          self.layer_post_mp = MLP(
              new_layer_config(
                  dim_in * 2,
                  dim_out,
                  cfg.gnn.layers_post_mp,
                  has_act=False,
                  has_bias=True,
                  cfg=cfg,
              )
          )
          self.decode_module = lambda v1, v2: self.layer_post_mp(
              torch.cat((v1, v2), dim=-1)
          )
      else:
          if dim_out > 1:
              raise ValueError(
                  f"Binary edge decoding "
                  f"'{cfg.model.edge_decoding}' is used for "
                  f"multi-class classification"
              )
          self.layer_post_mp = MLP(
              new_layer_config(
                  dim_in,
                  dim_in,
                  cfg.gnn.layers_post_mp,
                  has_act=False,
                  has_bias=True,
                  cfg=cfg,
              )
          )
          if cfg.model.edge_decoding == "dot":
              self.decode_module = lambda v1, v2: torch.sum(
                  v1 * v2, dim=-1
              )
          elif cfg.model.edge_decoding == "cosine_similarity":
              self.decode_module = torch.nn.CosineSimilarity(dim=-1)
          else:
              raise ValueError(
                  f"Unknown edge decoding "
                  f"'{cfg.model.edge_decoding}'"
              )
  
imports: import torch
  import torch_geometric.graphgym.register
  from torch_geometric.graphgym.config import cfg
  from torch_geometric.graphgym.models.layer import MLP, new_layer_config
  from torch_geometric.graphgym.register import register_head
label_desc: Use image node_id 1 to create a new GNNEdgeHead object from inherited base classes: torch.nn with example: obj = GNNEdgeHead(dim_in, dim_out)