Convolutional layer without summation over different channels

Assuming I have 5x5x3 image and I have different filter for each channel - for example 3x3x3.
In Cov2D first, each of the kernels in the filter are applied to three channels in the input layer, separately (which gives 3x3x3 - without padding and stride 1) and the these three channels are summed together (element-wise addition), gives 3x3x1.

I want instead of summation over channels (3x3x1), concatenate the three channels (3x3x3).

Thanks for help.