700abc65fd2172a2c6809dd9b72cf50fc2407772,allennlp/models/encoder_decoders/composed_seq2seq.py,ComposedSeq2Seq,__init__,#ComposedSeq2Seq#Any#Any#Any#Any#Any#Any#Any#,48

Before Change

                )

            source_embedder = self._source_text_embedder._token_embedders[tied_source_embedder_key]
            if not isinstance(source_embedder, Embedding):
                raise ConfigurationError(
                    "Unable to tie embeddings,"
                    "Selected source embedder is not an instance of `Embedding`."
                )
            if source_embedder.get_output_dim() != self._decoder.target_embedder.get_output_dim():
                raise ConfigurationError(
                    f"Output Dimensions mismatch between" f"source embedder and target embedder."
                )
            self._source_text_embedder._token_embedders[
                tied_source_embedder_key
            ] = self._decoder.target_embedder
        initializer(self)

    @overrides
    def forward(

After Change

        encoder: Seq2SeqEncoder,
        decoder: SeqDecoder,
        tied_source_embedder_key: Optional[str] = None,
        initializer: InitializerApplicator = InitializerApplicator(),
        **kwargs,
    ) -> None:

        super().__init__(vocab, **kwargs)

        self._source_text_embedder = source_text_embedder
        self._encoder = encoder
        self._decoder = decoder

        if self._encoder.get_output_dim() != self._decoder.get_output_dim():
            raise ConfigurationError(
                f"Encoder output dimension {self._encoder.get_output_dim()} should be"
                f" equal to decoder dimension {self._decoder.get_output_dim()}."
            )
        if tied_source_embedder_key:
            // A bit of a ugly hack to tie embeddings.

Instances