code: def forward(
      self,
      features: _typing.Sequence[torch.Tensor],
      graph: torch_geometric.data.Data,
      pos_edge: torch.Tensor,
      neg_edge: torch.Tensor,
      **__kwargs,
  ):
      z = features[-1]
      edge_index = torch.cat([pos_edge, neg_edge], dim=-1)
      logits = (z[edge_index[0]] * z[edge_index[1]]).sum(dim=-1)
      return logits
  
imports: import torch.nn.functional
  import typing
  import torch_geometric
  from torch_geometric.nn.conv import GraphConv
  from torch_geometric.nn.pool import TopKPooling
  from torch_geometric.nn.glob import global_add_pool, global_max_pool, global_mean_pool
  from ...encoders import base_encoder
  from ..None import base_decoder, decoder_registry
  from ...None import _utils
label_desc: Use image node_id 1 for calling the _DotProductLinkPredictonDecoder obj's underlying member method code with example usage: obj.forward(features, graph, pos_edge, neg_edge) and returns: logits