How to use the ampligraph.latent_features.constants.DEFAULT_BATCH_COUNT 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.

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={'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.

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={'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.

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={'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

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.

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.