class_docstr: Decoder for using inner product for prediction.
class_docstr_tok: ["Decoder","for","using","inner","product","for","prediction","."]
code: def forward(self, z):
      z = F.dropout(z, self.dropout, training=self.training)
      adj = self.act(torch.mm(z, z.t()))
      return adj
  
file_path: graph4nlp/graph4nlp/pytorch/test/link_prediction/model.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from layers import GraphConvolution
  from ...modules.prediction.classification.link_prediction.ConcatFeedForwardNNLayer import ConcatFeedForwardNNLayer
  from ...modules.prediction.classification.link_prediction.ElementSumLayer import ElementSumLayer
  from ...modules.prediction.classification.link_prediction.StackedElementProdLayer import StackedElementProdLayer
label_desc: Use image node_id 2 for calling the InnerProductDecoder obj's underlying member method code with example usage: obj.forward(z) and returns: adj