c88688032b3173bb140677e0e4e7b481efd3a5b9,onmt/Models.py,Encoder,forward,#Encoder#Any#Any#Any#,126

Before Change

        if self.encoder_layer == "mean":
            // No RNN, just take mean as final state.
            mean = emb.mean(0).unsqueeze(0) \
                   .expand(self.layers, n_batch, vec_size)
            return (mean, mean), emb

After Change

        
        // CHECKS
        s_len, n_batch, n_feats = input.size()
        if lengths is not None:
            n_batch_ = lengths.size()
            aeq(n_batch, n_batch_)
        // END CHECKS


        
        emb = self.embeddings(input)
        s_len, n_batch, vec_size = emb.size()

        if self.encoder_layer == "mean":

Instances