How to use the ampligraph.latent_features.initializers.DEFAULT_XAVIER_IS_UNIFORM function in ampligraph

def __init__(self,
                 k=constants.DEFAULT_EMBEDDING_SIZE,
                 eta=constants.DEFAULT_ETA,
                 epochs=constants.DEFAULT_EPOCH,
                 batches_count=constants.DEFAULT_BATCH_COUNT,
                 seed=constants.DEFAULT_SEED,
                 embedding_model_params={'negative_corruption_entities': constants.DEFAULT_CORRUPTION_ENTITIES,
                                         'corrupt_sides': constants.DEFAULT_CORRUPT_SIDE_TRAIN},
                 optimizer=constants.DEFAULT_OPTIM,
                 optimizer_params={'lr': constants.DEFAULT_LR},
                 loss=constants.DEFAULT_LOSS,
                 loss_params={},
                 regularizer=constants.DEFAULT_REGULARIZER,
                 regularizer_params={},
                 initializer=constants.DEFAULT_INITIALIZER,
                 initializer_params={'uniform': DEFAULT_XAVIER_IS_UNIFORM},
                 verbose=constants.DEFAULT_VERBOSE):
        """Initialize an EmbeddingModel

        Also creates a new Tensorflow session for training.

        Parameters
        ----------
        k : int
            Embedding space dimensionality
        eta : int
            The number of negatives that must be generated at runtime during training for each positive.
        epochs : int
            The iterations of the training loop.
        batches_count : int
            The number of batches in which the training set must be split during the training loop.
        seed : int

k=constants.DEFAULT_EMBEDDING_SIZE,
                 eta=constants.DEFAULT_ETA,
                 epochs=constants.DEFAULT_EPOCH,
                 batches_count=constants.DEFAULT_BATCH_COUNT,
                 seed=constants.DEFAULT_SEED,
                 embedding_model_params={'num_filters': 32,
                                         'filter_sizes': [1],
                                         'dropout': 0.1},
                 optimizer=constants.DEFAULT_OPTIM,
                 optimizer_params={'lr': constants.DEFAULT_LR},
                 loss=constants.DEFAULT_LOSS,
                 loss_params={},
                 regularizer=constants.DEFAULT_REGULARIZER,
                 regularizer_params={},
                 initializer=constants.DEFAULT_INITIALIZER,
                 initializer_params={'uniform': DEFAULT_XAVIER_IS_UNIFORM},
                 large_graphs=False,
                 verbose=constants.DEFAULT_VERBOSE):
        """Initialize an EmbeddingModel

        Parameters
        ----------
        k : int
            Embedding space dimensionality.

        eta : int
            The number of negatives that must be generated at runtime during training for each positive.

        epochs : int
            The iterations of the training loop.

        batches_count : int

def __init__(self,
                 k=DEFAULT_EMBEDDING_SIZE,
                 eta=DEFAULT_ETA,
                 epochs=DEFAULT_EPOCH,
                 batches_count=DEFAULT_BATCH_COUNT,
                 seed=DEFAULT_SEED,
                 embedding_model_params={'negative_corruption_entities': DEFAULT_CORRUPTION_ENTITIES,
                                         'corrupt_sides': DEFAULT_CORRUPT_SIDE_TRAIN},
                 optimizer=DEFAULT_OPTIM,
                 optimizer_params={'lr': DEFAULT_LR},
                 loss=DEFAULT_LOSS,
                 loss_params={},
                 regularizer=DEFAULT_REGULARIZER,
                 regularizer_params={},
                 initializer=DEFAULT_INITIALIZER,
                 initializer_params={'uniform': DEFAULT_XAVIER_IS_UNIFORM},
                 verbose=DEFAULT_VERBOSE):
        """Initialize an EmbeddingModel

        Also creates a new Tensorflow session for training.

        Parameters
        ----------
        k : int
            Embedding space dimensionality
        eta : int
            The number of negatives that must be generated at runtime during
            training for each positive.
        epochs : int
            The iterations of the training loop.
        batches_count : int
            The number of batches in which the training set must be split

k=constants.DEFAULT_EMBEDDING_SIZE,
                 eta=constants.DEFAULT_ETA,
                 epochs=constants.DEFAULT_EPOCH,
                 batches_count=constants.DEFAULT_BATCH_COUNT,
                 seed=constants.DEFAULT_SEED,
                 embedding_model_params={'normalize_ent_emb': constants.DEFAULT_NORMALIZE_EMBEDDINGS,
                                         'negative_corruption_entities': constants.DEFAULT_CORRUPTION_ENTITIES,
                                         'corrupt_sides': constants.DEFAULT_CORRUPT_SIDE_TRAIN},
                 optimizer=constants.DEFAULT_OPTIM,
                 optimizer_params={'lr': constants.DEFAULT_LR},
                 loss=constants.DEFAULT_LOSS,
                 loss_params={},
                 regularizer=constants.DEFAULT_REGULARIZER,
                 regularizer_params={},
                 initializer=constants.DEFAULT_INITIALIZER,
                 initializer_params={'uniform': DEFAULT_XAVIER_IS_UNIFORM},
                 verbose=constants.DEFAULT_VERBOSE):
        """Initialize an EmbeddingModel

        Also creates a new Tensorflow session for training.

        Parameters
        ----------
        k : int
            Embedding space dimensionality
        eta : int
            The number of negatives that must be generated at runtime during training for each positive.
        epochs : int
            The iterations of the training loop.
        batches_count : int
            The number of batches in which the training set must be split during the training loop.
        seed : int

eta=constants.DEFAULT_ETA,
                 epochs=constants.DEFAULT_EPOCH,
                 batches_count=constants.DEFAULT_BATCH_COUNT,
                 seed=constants.DEFAULT_SEED,
                 embedding_model_params={'norm': constants.DEFAULT_NORM_TRANSE,
                                         'normalize_ent_emb': constants.DEFAULT_NORMALIZE_EMBEDDINGS,
                                         'negative_corruption_entities': constants.DEFAULT_CORRUPTION_ENTITIES,
                                         'corrupt_sides': constants.DEFAULT_CORRUPT_SIDE_TRAIN},
                 optimizer=constants.DEFAULT_OPTIM,
                 optimizer_params={'lr': constants.DEFAULT_LR},
                 loss=constants.DEFAULT_LOSS,
                 loss_params={},
                 regularizer=constants.DEFAULT_REGULARIZER,
                 regularizer_params={},
                 initializer=constants.DEFAULT_INITIALIZER,
                 initializer_params={'uniform': DEFAULT_XAVIER_IS_UNIFORM},
                 verbose=constants.DEFAULT_VERBOSE):
        """
        Initialize an EmbeddingModel.

        Also creates a new Tensorflow session for training.

        Parameters
        ----------
        k : int
            Embedding space dimensionality.
        eta : int
            The number of negatives that must be generated at runtime during training for each positive.
        epochs : int
            The iterations of the training loop.
        batches_count : int
            The number of batches in which the training set must be split during the training loop.

def __init__(self,
                 k=constants.DEFAULT_EMBEDDING_SIZE,
                 eta=constants.DEFAULT_ETA,
                 epochs=constants.DEFAULT_EPOCH,
                 batches_count=constants.DEFAULT_BATCH_COUNT,
                 seed=constants.DEFAULT_SEED,
                 embedding_model_params={'negative_corruption_entities': constants.DEFAULT_CORRUPTION_ENTITIES,
                                         'corrupt_sides': constants.DEFAULT_CORRUPT_SIDE_TRAIN},
                 optimizer=constants.DEFAULT_OPTIM,
                 optimizer_params={'lr': constants.DEFAULT_LR},
                 loss=constants.DEFAULT_LOSS,
                 loss_params={},
                 regularizer=constants.DEFAULT_REGULARIZER,
                 regularizer_params={},
                 initializer=constants.DEFAULT_INITIALIZER,
                 initializer_params={'uniform': DEFAULT_XAVIER_IS_UNIFORM},
                 verbose=constants.DEFAULT_VERBOSE):
        """Initialize an EmbeddingModel

        Also creates a new Tensorflow session for training.

        Parameters
        ----------
        k : int
            Embedding space dimensionality
        eta : int
            The number of negatives that must be generated at runtime during training for each positive.
        epochs : int
            The iterations of the training loop.
        batches_count : int
            The number of batches in which the training set must be split during the training loop.
        seed : int

def __init__(self,
                 k=constants.DEFAULT_EMBEDDING_SIZE,
                 eta=constants.DEFAULT_ETA,
                 epochs=constants.DEFAULT_EPOCH,
                 batches_count=constants.DEFAULT_BATCH_COUNT,
                 seed=constants.DEFAULT_SEED,
                 embedding_model_params={},
                 optimizer=constants.DEFAULT_OPTIM,
                 optimizer_params={'lr': constants.DEFAULT_LR},
                 loss=constants.DEFAULT_LOSS,
                 loss_params={},
                 regularizer=constants.DEFAULT_REGULARIZER,
                 regularizer_params={},
                 initializer=constants.DEFAULT_INITIALIZER,
                 initializer_params={'uniform': DEFAULT_XAVIER_IS_UNIFORM},
                 large_graphs=False,
                 verbose=constants.DEFAULT_VERBOSE):
        """Initialize an EmbeddingModel

        Also creates a new Tensorflow session for training.

        Parameters
        ----------
        k : int
            Embedding space dimensionality.

        eta : int
            The number of negatives that must be generated at runtime during training for each positive.

        epochs : int
            The iterations of the training loop.