import tensorflow as tf

    B1 = euclidean_norm_squared(X, axis=1)/(2+4*y)
    return 1/tf.sqrt(1+y) + A*tf.reduce_sum(__phi(A1)) - B*tf.reduce_sum(__phi(B1))


def cw(X, y=None):
    D = tf.cast(tf.shape(X)[1], tf.float32)
    N = tf.cast(tf.shape(X)[0], tf.float32)
    if y is None:
        y = silverman_rule_of_thumb(N)

    K = 1/(2*D-3)

