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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def masking_noise(data, sess, v):
 """Apply masking noise to data in X. In other words a fraction v of elements of X (chosen at random) is forced to zero. :param data: array_like, Input data :param sess: TensorFlow session :param v: fraction of elements to distort, float :return: transformed data """

    data_noise = data.copy()
    rand = tf.random_uniform(data.shape)
    data_noise[sess.run(tf.nn.relu(tf.sign(v - rand))).astype(np.bool)] = 0

    return data_noise