How to adapt the output size of a given feature map in the Deep Learning Toolbox by using the "pool" operation?

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cui on 26 Oct 2021
I understand that there are currently "averagePooling2dLayer, maxPooling2dLayer, globalAveragePooling2dLayer, globalMaxPooling2dLayer" and so on for the "pool" operation. globalMaxPooling2dLayer", "maxpool" and other direct call functions, but nothing like pytorch's "adaptive_max_pool2d". function that can directly specify the size of the output feature map for a pool operation?
The following simple example is pytorch code, how can matlab achieve the same purpose?
input = torch.rand(8,3,224,224) # input tensor, NCHW
outSize = (20,20) # specify output tensor size, H_out*W_out
output = F.adaptive_max_pool2d(input,outSize) # adaptive output
print(output.shape) # result--> torch.Size([8, 3, 20, 20])

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