ludwig/encoders/sequence_encoders.py [414:449]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
            )

        # The user is expected to provide fc_layers or num_fc_layers
        # The following logic handles the case where the user either provides
        # both or neither.
        if fc_layers is None and num_fc_layers is None:
            # use default layers with varying filter sizes
            fc_layers = [
                {'fc_size': 512},
                {'fc_size': 256}
            ]
            num_fc_layers = 2
        elif fc_layers is not None and num_fc_layers is not None:
            raise ValueError(
                'Invalid layer parametrization, use either fc_layers or '
                'num_fc_layers only. Not both.'
            )

        self.reduce_output = reduce_output
        self.reduce_sequence = SequenceReducer(reduce_mode=reduce_output)
        self.should_embed = should_embed
        self.embed_sequence = None

        if self.should_embed:
            logger.debug('  EmbedSequence')
            self.embed_sequence = EmbedSequence(
                vocab,
                embedding_size,
                representation=representation,
                embeddings_trainable=embeddings_trainable,
                pretrained_embeddings=pretrained_embeddings,
                embeddings_on_cpu=embeddings_on_cpu,
                dropout=dropout,
                embedding_initializer=weights_initializer,
                embedding_regularizer=weights_regularizer
            )
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ludwig/encoders/sequence_encoders.py [761:796]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
            )

        # The user is expected to provide fc_layers or num_fc_layers
        # The following logic handles the case where the user either provides
        # both or neither.
        if fc_layers is None and num_fc_layers is None:
            # use default layers with varying filter sizes
            fc_layers = [
                {'fc_size': 512},
                {'fc_size': 256}
            ]
            num_fc_layers = 2
        elif fc_layers is not None and num_fc_layers is not None:
            raise ValueError(
                'Invalid layer parametrization, use either fc_layers or '
                'num_fc_layers only. Not both.'
            )

        self.reduce_output = reduce_output
        self.reduce_sequence = SequenceReducer(reduce_mode=reduce_output)
        self.should_embed = should_embed
        self.embed_sequence = None

        if self.should_embed:
            logger.debug('  EmbedSequence')
            self.embed_sequence = EmbedSequence(
                vocab,
                embedding_size,
                representation=representation,
                embeddings_trainable=embeddings_trainable,
                pretrained_embeddings=pretrained_embeddings,
                embeddings_on_cpu=embeddings_on_cpu,
                dropout=dropout,
                embedding_initializer=weights_initializer,
                embedding_regularizer=weights_regularizer
            )
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ludwig/encoders/sequence_encoders.py [1108:1143]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
            )

        # The user is expected to provide fc_layers or num_fc_layers
        # The following logic handles the case where the user either provides
        # both or neither.
        if fc_layers is None and num_fc_layers is None:
            # use default layers with varying filter sizes
            fc_layers = [
                {'fc_size': 512},
                {'fc_size': 256}
            ]
            num_fc_layers = 2
        elif fc_layers is not None and num_fc_layers is not None:
            raise ValueError(
                'Invalid layer parametrization, use either fc_layers or '
                'num_fc_layers only. Not both.'
            )

        self.reduce_output = reduce_output
        self.reduce_sequence = SequenceReducer(reduce_mode=reduce_output)
        self.should_embed = should_embed
        self.embed_sequence = None

        if self.should_embed:
            logger.debug('  EmbedSequence')
            self.embed_sequence = EmbedSequence(
                vocab,
                embedding_size,
                representation=representation,
                embeddings_trainable=embeddings_trainable,
                pretrained_embeddings=pretrained_embeddings,
                embeddings_on_cpu=embeddings_on_cpu,
                dropout=dropout,
                embedding_initializer=weights_initializer,
                embedding_regularizer=weights_regularizer
            )
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