f5278f68882ebf89a013611c86eb20345e723df2,pytext/models/seq_models/seq2seq_model.py,Seq2SeqModel,forward,#Seq2SeqModel#Any#Any#Any#Any#,164

Before Change

        if dict_feats:
            (
                output_dict["dict_tokens"],
                output_dict["dict_weights"],
                output_dict["dict_lengths"],
            ) = dict_feats

After Change

            src_embedding_list.append(
                create_module(config.dict_embedding, tensorizer=gazetteer_tensorizer)
            )
        contextual_token_tensorizer = tensorizers.get("contextual_token_embedding")
        if contextual_token_tensorizer:
            src_embedding_list.append(
                create_module(
                    config.contextual_token_embedding,
                    tensorizer=contextual_token_tensorizer,
                )
            )
        source_embedding = ScriptableEmbeddingList(src_embedding_list)

        trg_tokens = tensorizers["trg_seq_tokens"]
        target_embedding = ScriptableEmbeddingList(
            [create_module(config.target_embedding, tensorizer=trg_tokens)]
        )

        model = create_module(
            config.encoder_decoder,
            src_tokens.vocab,
            source_embedding,

Instances