When the input data contains multiple channels, denoted as $c_ extrm{i}$, the convolution kernel must have the same number of input channels to perform cross-correlation. If the kernel's two-dimensional spatial window shape is $k_ extrm{h} imes k_ extrm{w}$, a kernel tensor of shape $k_ extrm{h} imes k_ extrm{w}$ is required for every input channel. Concatenating these $c_ extrm{i}$ tensors yields a convolution kernel with an overall shape of $c_ extrm{i} imes k_ extrm{h} imes k_ extrm{w}$.