import tensorflow as tf

    def __init__(
            self,
            inputs=None,
            train_labels=None,
            vocabulary_size=80000,
            embedding_size=200,
            num_sampled=64,
            nce_loss_args=None,
            E_init=tf.random_uniform_initializer(minval=-1.0, maxval=1.0),
            E_init_args=None,
            nce_W_init=tf.truncated_normal_initializer(stddev=0.03),
            nce_W_init_args=None,
            nce_b_init=tf.constant_initializer(value=0.0),
            nce_b_init_args=None,
            name='word2vec',
    ):
        if nce_loss_args is None:
            nce_loss_args = {}
        if E_init_args is None:
            E_init_args = {}
