def add_position_embedding()

in train.py [0:0]

• 13 lines of code
• 2 McCabe index (conditional complexity)

def add_position_embedding(x, x_emb, train, step):
    num_e = H.emb_number
    emb_std = H.pos_embd_std * np.sqrt(1.0 / num_e)
    for idx in range(H.emb_number):
        vsize = H.emb_vocabs[idx]
        name = f"pos_emb_{idx}"
        we = tf.get_variable(
            name, [vsize, H.n_embd], dtype=H.dtype,
            initializer=random_or_zeros_init(stddev=emb_std))
        e = bs.embedding_lookup(we, x_emb[:, idx, :])
        e = embedding_dropout(e, train)
        x += e
    return x