1e05cf469de2a71529d5f6ce922a37e0a386c987,batchflow/models/tf/encoder_decoder.py,EncoderDecoder,body,#Any#Any#Any#,137

Before Change

        encoder = kwargs.pop("encoder")
        embeddings = kwargs.get("embeddings")
        embeddings_common = embeddings.pop("common")
        embeddings_order = embeddings.pop("order") or [name for name in kwargs
                                                       if "embedding" in name]
        decoder = kwargs.pop("decoder")

        with tf.variable_scope(name):
            // Encoder: transition down
            encoder_args = {**kwargs, **encoder}
            encoder_outputs = cls.encoder(inputs, name="encoder", **encoder_args)
            x = encoder_outputs[-1]

            // Bottleneck: working with compressed representation via multiple steps of processing
            for embedding_name in embeddings_order:
                embedding_args = kwargs.pop(embedding_name)
                if embedding_args:
                    embedding_args = {**kwargs, **embeddings_common, **embedding_args}
                    x = cls.embedding(x, name=embedding_name, **embedding_args)

            encoder_outputs.append(x)

            // Decoder: transition up
            decoder_args = {**kwargs, **decoder}

After Change

            // Bottleneck: working with compressed representation via multiple steps of processing
            embeddings = embeddings if isinstance(embeddings, list) else [embeddings]

            for i, embedding in enumerate(embeddings):
                embedding_args = {**kwargs, **embedding}
                x = cls.embedding(x, name="embedding"+str(i), **embedding_args)

            encoder_outputs.append(x)

            // Decoder: transition up
            decoder_args = {**kwargs, **decoder}

Instances