Tf.strided_slice(), what does new_axis_mask do?

General Discussion
,
#1

Hello, I am quite confused about the parameter new_axis_mask of tf.strided_slice() function. The official documentation says:

If the ith bit of new_axis_mask is set, then begin, end, and stride are ignored and a new length 1 dimension is added at this point in the output tensor.
For example, foo[:4, tf.newaxis, :2] would produce a shape (4, 1, 2) tensor.

I did some tests…

import tensorflow as tf
import numpy as np

arr = np.random.rand(4,6,7)
out1 = tf.strided_slice(arr, begin=[1,1,1], end=[4,5,6], strides=[1,1,1])
print(out1.shape) // returns TensorShape([3, 4, 5])

So according to my understanding the result of(when using new_axis_mask)

out2 = tf.strided_slice(arr, begin=[1,1,1], end=[4,5,6], strides=[1,1,1], new_axis_mask=1)
print(out2.shape)

should be TensorShape([1, 3, 4, 5]) cause we add an extra dimension to the output at the first axis. However the result is TensorShape([1, 3, 5, 7]).

Can anyone explain me how the parameter new_axis_mask of tf.strided_slice() function works?
Code is here: Google Colab