can you tell why there are zero trainable parameters in spatial attention layers?

in the notebook 3.3 build cnn with attention ,if we see the summary ,there are zero trainable parameters.

`Layer (type) Output Shape Param #

conv2d_6 (Conv2D) (None, 32, 32, 32) 896

batch_normalization_4 (Batch (None, 32, 32, 32) 128

conv2d_7 (Conv2D) (None, 32, 32, 32) 9248

batch_normalization_5 (Batch (None, 32, 32, 32) 128

channel_attention (ChannelAt (None, 32, 32, 32) 0

spatial_attention (SpatialAt (None, 32, 32, 32) 0

max_pooling2d_2 (MaxPooling2 (None, 16, 16, 32) 0

conv2d_8 (Conv2D) (None, 16, 16, 64) 18496

batch_normalization_6 (Batch (None, 16, 16, 64) 256

conv2d_9 (Conv2D) (None, 16, 16, 64) 36928

batch_normalization_7 (Batch (None, 16, 16, 64) 256

channel_attention_1 (Channel (None, 16, 16, 64) 0

spatial_attention_1 (Spatial (None, 16, 16, 64) 0

max_pooling2d_3 (MaxPooling2 (None, 8, 8, 64) 0

conv2d_10 (Conv2D) (None, 8, 8, 128) 73856

batch_normalization_8 (Batch (None, 8, 8, 128) 512

conv2d_11 (Conv2D) (None, 8, 8, 128) 147584

channel_attention_2 (Channel (None, 8, 8, 128) 0

spatial_attention_2 (Spatial (None, 8, 8, 128) 0

global_average_pooling2d_1 ( (None, 128) 0

dense_1 (Dense) (None, 10) 1290

Total params: 289,578
Trainable params: 288,938
Non-trainable params: 640
_________________________________________________________________`

In the paper + the video https://www.youtube.com/watch?v=F7wd4wQyPd8 you mention using layer normalization

To prevent this, FF normalizes the length of the hidden vector before using
it as input to the next layer (Ba et al., 2016b; Carandini and Heeger, 2013) This removes all of the
information that was used to determine the goodness in the first hidden layer and forces the next
hidden layer to use information in the relative activities of the neurons in the first hidden layer. These
relative activities are unaffected by the layer-normalization

However I did not notice layer norm in your implementation. I see that you used per layer training where each layer does not use previous layers outputs which probably achieves similar outcome?, why did you decide to use that instead of layer normalization? or was the reason for not using layernorm that you only have 1 hidden layer?

Thank for sharing the code. when i run your code in spyder IDE it give me error in the "class ChannelAttention" class. How to remove this error. Thank you.
Bellow is the visual of your code.