About the convolutional layer

https://stackoverflow.com/questions/42786717/how-to-calculate-the-number-of-parameters-for-convolutional-neural-network

I have a question because there was something I didn't understand while looking at the website above.

#name size parameters
--- -------- ------------------------- ------------------------
0 input 1x28x280
1 conv2d1(28-(5-1)=24->32x24x24(5*5*1+1)*32=832
2 maxpool 132x12x120
3 conv2d2(12-(3-1)=10->32x10x10(3*3*32+1)*32=9'248
4 maxpool 232x5x50
5dense256 (32*5*5+1)*256 = 205'056
6 output 10 (256+1)*10 = 2'570

When you have a CNN model like the one above, you have 32 filters in the first conv2d1 layer, right?
This was understood to be 32 different 5*5 weight matrices.

By biting the pooling layer, you can see that the output is 32*12*12.
(like 32 12*12 images)

However, if I apply 32 weight matrix filters to this output in conv2d2, it will be 32 times larger, so is that correct?

Thank you for your cooperation.