How to use the ludwig.models.modules.embedding_modules.Embed function in ludwig

a key `type` that identifies the type of initialzier and
                    other keys for its parameters, e.g.
                    `{type: normal, mean: 0, stddev: 0}`.
                    To know the parameters of each initializer, please refer to
                    TensorFlow's documentation.
            :type initializer: str
            :param regularize: if `True` the embedding wieghts are added to
                   the set of weights that get reularized by a regularization
                   loss (if the `regularization_lambda` in `training`
                   is greater than 0).
            :type regularize: Boolean
        """
        self.embedding_size = embedding_size
        self.reduce_output = reduce_output

        self.embed_mode = Embed(
            [str(i) for i in range(3)],
            embedding_size,
            representation='dense',
            embeddings_trainable=True,
            pretrained_embeddings=None,
            force_embedding_size=True,
            embeddings_on_cpu=embeddings_on_cpu,
            dropout=dropout,
            initializer=initializer,
            regularize=regularize
        )
        self.embed_edge = Embed(
            [str(i) for i in range(7)],
            embedding_size,
            representation='dense',
            embeddings_trainable=True,

dropout=dropout,
            initializer=initializer,
            regularize=regularize
        )
        self.embed_day = Embed(
            [str(i) for i in range(31)],
            embedding_size,
            representation='dense',
            embeddings_trainable=True,
            pretrained_embeddings=None,
            embeddings_on_cpu=embeddings_on_cpu,
            dropout=dropout,
            initializer=initializer,
            regularize=regularize
        )
        self.embed_weekday = Embed(
            [str(i) for i in range(7)],
            embedding_size,
            representation='dense',
            embeddings_trainable=True,
            pretrained_embeddings=None,
            embeddings_on_cpu=embeddings_on_cpu,
            dropout=dropout,
            initializer=initializer,
            regularize=regularize
        )
        self.embed_yearday = Embed(
            [str(i) for i in range(366)],
            embedding_size,
            representation='dense',
            embeddings_trainable=True,
            pretrained_embeddings=None,

self.embedding_size = embedding_size
        self.reduce_output = reduce_output

        self.embed_mode = Embed(
            [str(i) for i in range(3)],
            embedding_size,
            representation='dense',
            embeddings_trainable=True,
            pretrained_embeddings=None,
            force_embedding_size=True,
            embeddings_on_cpu=embeddings_on_cpu,
            dropout=dropout,
            initializer=initializer,
            regularize=regularize
        )
        self.embed_edge = Embed(
            [str(i) for i in range(7)],
            embedding_size,
            representation='dense',
            embeddings_trainable=True,
            pretrained_embeddings=None,
            force_embedding_size=True,
            embeddings_on_cpu=embeddings_on_cpu,
            dropout=dropout,
            initializer=initializer,
            regularize=regularize
        )
        self.embed_resolution = Embed(
            [str(i) for i in range(16)],
            embedding_size,
            representation='dense',
            embeddings_trainable=True,

initializer=initializer,
            regularize=regularize
        )
        self.embed_base_cell = Embed(
            [str(i) for i in range(122)],
            embedding_size,
            representation='dense',
            embeddings_trainable=True,
            pretrained_embeddings=None,
            force_embedding_size=True,
            embeddings_on_cpu=embeddings_on_cpu,
            dropout=dropout,
            initializer=initializer,
            regularize=regularize
        )
        self.embed_cells = Embed(
            [str(i) for i in range(8)],
            embedding_size,
            representation='dense',
            embeddings_trainable=True,
            pretrained_embeddings=None,
            force_embedding_size=True,
            embeddings_on_cpu=embeddings_on_cpu,
            dropout=dropout,
            initializer=initializer,
            regularize=regularize
        )

        self.fc_stack = FCStack(
            layers=fc_layers,
            num_layers=num_fc_layers,
            default_fc_size=fc_size,

dropout=dropout,
            initializer=initializer,
            regularize=regularize
        )
        self.embed_weekday = Embed(
            [str(i) for i in range(7)],
            embedding_size,
            representation='dense',
            embeddings_trainable=True,
            pretrained_embeddings=None,
            embeddings_on_cpu=embeddings_on_cpu,
            dropout=dropout,
            initializer=initializer,
            regularize=regularize
        )
        self.embed_yearday = Embed(
            [str(i) for i in range(366)],
            embedding_size,
            representation='dense',
            embeddings_trainable=True,
            pretrained_embeddings=None,
            embeddings_on_cpu=embeddings_on_cpu,
            dropout=dropout,
            initializer=initializer,
            regularize=regularize
        )
        self.embed_hour = Embed(
            [str(i) for i in range(24)],
            embedding_size,
            representation='dense',
            embeddings_trainable=True,
            pretrained_embeddings=None,

:param regularize: if `True` the embedding wieghts are added to
                   the set of weights that get reularized by a regularization
                   loss (if the `regularization_lambda` in `training`
                   is greater than 0).
            :type regularize: Boolean
        """
        self.year_fc = FCStack(
            num_layers=1,
            default_fc_size=1,
            default_activation=None,
            default_norm=None,
            default_dropout=dropout,
            default_regularize=regularize,
            default_initializer=initializer
        )
        self.embed_month = Embed(
            [str(i) for i in range(12)],
            embedding_size,
            representation='dense',
            embeddings_trainable=True,
            pretrained_embeddings=None,
            embeddings_on_cpu=embeddings_on_cpu,
            dropout=dropout,
            initializer=initializer,
            regularize=regularize
        )
        self.embed_day = Embed(
            [str(i) for i in range(31)],
            embedding_size,
            representation='dense',
            embeddings_trainable=True,
            pretrained_embeddings=None,

def __init__(
            self,
            vocab,
            embedding_size,
            representation='dense',
            embeddings_trainable=True,
            pretrained_embeddings=None,
            force_embedding_size=False,
            embeddings_on_cpu=False,
            mask=True,
            dropout=False,
            initializer=None,
            regularize=True
    ):
        self.embed = Embed(
            vocab,
            embedding_size,
            representation=representation,
            embeddings_trainable=embeddings_trainable,
            pretrained_embeddings=pretrained_embeddings,
            force_embedding_size=force_embedding_size,
            embeddings_on_cpu=embeddings_on_cpu,
            dropout=dropout,
            initializer=initializer,
            regularize=regularize
        )

        self.mask = mask

initializer=initializer,
            regularize=regularize
        )
        self.embed_resolution = Embed(
            [str(i) for i in range(16)],
            embedding_size,
            representation='dense',
            embeddings_trainable=True,
            pretrained_embeddings=None,
            force_embedding_size=True,
            embeddings_on_cpu=embeddings_on_cpu,
            dropout=dropout,
            initializer=initializer,
            regularize=regularize
        )
        self.embed_base_cell = Embed(
            [str(i) for i in range(122)],
            embedding_size,
            representation='dense',
            embeddings_trainable=True,
            pretrained_embeddings=None,
            force_embedding_size=True,
            embeddings_on_cpu=embeddings_on_cpu,
            dropout=dropout,
            initializer=initializer,
            regularize=regularize
        )
        self.embed_cells = Embed(
            [str(i) for i in range(8)],
            embedding_size,
            representation='dense',
            embeddings_trainable=True,

default_dropout=dropout,
            default_regularize=regularize,
            default_initializer=initializer
        )
        self.embed_month = Embed(
            [str(i) for i in range(12)],
            embedding_size,
            representation='dense',
            embeddings_trainable=True,
            pretrained_embeddings=None,
            embeddings_on_cpu=embeddings_on_cpu,
            dropout=dropout,
            initializer=initializer,
            regularize=regularize
        )
        self.embed_day = Embed(
            [str(i) for i in range(31)],
            embedding_size,
            representation='dense',
            embeddings_trainable=True,
            pretrained_embeddings=None,
            embeddings_on_cpu=embeddings_on_cpu,
            dropout=dropout,
            initializer=initializer,
            regularize=regularize
        )
        self.embed_weekday = Embed(
            [str(i) for i in range(7)],
            embedding_size,
            representation='dense',
            embeddings_trainable=True,
            pretrained_embeddings=None,

super().__init__(feature)

        self.vocab = []

        self.embedding_size = 50
        self.representation = 'dense'
        self.embeddings_trainable = True
        self.pretrained_embeddings = None
        self.embeddings_on_cpu = False
        self.dropout = False
        self.initializer = None
        self.regularize = True

        _ = self.overwrite_defaults(feature)

        self.embed = Embed(
            vocab=self.vocab,
            embedding_size=self.embedding_size,
            representation=self.representation,
            embeddings_trainable=self.embeddings_trainable,
            pretrained_embeddings=self.pretrained_embeddings,
            embeddings_on_cpu=self.embeddings_on_cpu,
            dropout=self.dropout,
            initializer=self.initializer,
            regularize=self.regularize
        )