How to create a 3d kernel over channels?

Lets say I give two input epicures of size 234x234. Then I have an input tensor [2,3,234,234]. How do I create a 3d kernel which only take the 3 channels as input and produces 1 output feature map from it?

You could create an nn.Conv2d layer accepting inputs with 3 channels and creating a single output activation map as seen here:

x = torch.randn(2, 3, 234, 234)
conv = nn.Conv2d(in_channels=3, out_channels=1, kernel_size=3, padding=1)
out = conv(x)
# torch.Size([2, 1, 234, 234])

Note that the filter will be a 4D filter defined as [out_channels, in_channels, height, width].