Changing order of input dimension in Tensorflow 3-D Layers

According to the official documentation of tf.keras.layers.Conv3D

5+D tensor with shape: batch_shape + (channels, conv_dim1, conv_dim2,
conv_dim3) if data_format=’channels_first’ or 5+D tensor with shape:
batch_shape + (conv_dim1, conv_dim2, conv_dim3, channels) if
data_format=’channels_last’

.
Now the whole idea around channels and batch shape makes sense, but will changing the general order of
(conv_dim1, conv_dim2,conv_dim2) as (x,y,z) to say (z,x,y) affect the performance.

Does Conv3D worry about order of x-y-z dimension ?

I was training a U-net segmentation model and upon changing the order of axis I saw difference in performance. (x,y,z) order converges faster as compared to (y,x,z).

I just wanted to make sure what’s the correct way..