code: def weight_targeting(w, k):
    
    k = tf.to_int32(k)
    w_shape = shape_list(w)
    size = tf.to_int32(tf.reduce_prod(w_shape[:-1]))
    w = tf.reshape(w, [size, w_shape[-1]])
  
    transpose_w = tf.transpose(w)
    thres = tf.contrib.framework.sort(tf.abs(transpose_w), axis=1)[:, k]
    mask = to_float(thres[None, :] >= tf.abs(w))
  
    return tf.reshape(mask, w_shape)
docs: Weight-level magnitude pruning.