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This can be achieved using the tf.while_loop() and standard tuples as per the second example in the documentation.

这可以通过使用tf.while_loop()和标准元组来实现，这是文档中第二个例子。

def rosenbrock(data_tensor):
    columns = tf.unstack(data_tensor)

    # Track both the loop index and summation in a tuple in the form (index, summation)
    index_summation = (tf.constant(1), tf.constant(0.0))

    # The loop condition, note the loop condition is 'i < n-1'
    def condition(index, summation):
        return tf.less(index, tf.subtract(tf.shape(columns)[0], 1))

    # The loop body, this will return a result tuple in the same form (index, summation)
    def body(index, summation):
        x_i = tf.gather(columns, index)
        x_ip1 = tf.gather(columns, tf.add(index, 1))

        first_term = tf.square(tf.subtract(x_ip1, tf.square(x_i)))
        second_term = tf.square(tf.subtract(x_i, 1.0))
        summand = tf.add(tf.multiply(100.0, first_term), second_term)

        return tf.add(index, 1), tf.add(summation, summand)

    # We do not care about the index value here, return only the summation
    return tf.while_loop(condition, body, index_summation)[1]

It is important to note that the index increment should occur in the body of the loop similar to a standard while loop. In the solution given, it is the first item in the tuple returned by the body() function.

需要注意的是，索引增量应该出现在类似于标准while循环的循环体中。在给定的解决方案中，它是body()函数返回的tuple中的第一项。

Additionally, the loop condition function must allot a parameter for the summation although it is not used in this particular example.

此外，循环条件函数还必须为求和提供一个参数，尽管在这个特殊的例子中没有使用它。