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Given the following text problem statement, write Python code to implement the functionality described below in problem statement
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Problem:
I'm using tensorflow 2.10.0.
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Python Code:
import tensorflow as tf
x = [[[[1, 2, 3], [2, 3, 4], [0, 0, 0]],
      [[1, 2, 3], [2, 0, 4], [3, 4, 5]],
      [[1, 2, 3], [0, 0, 0], [0, 0, 0]],
      [[1, 2, 3], [1, 2, 3], [0, 0, 0]]],
     [[[1, 2, 3], [0, 1, 0], [0, 0, 0]],
      [[1, 2, 3], [2, 3, 4], [0, 0, 0]],
      [[1, 2, 3], [0, 0, 0], [0, 0, 0]],
      [[1, 2, 3], [1, 2, 3], [1, 2, 3]]]]
x = tf.convert_to_tensor(x, dtype=tf.float32)
def g(x):
    non_zero = tf.cast(x != 0, tf.float32)
    y = tf.reduce_sum(x, axis=-2) / tf.reduce_sum(non_zero, axis=-2)
    return y
result = g(x.__copy__())
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