The “RGB channels” would be located in dim1 so would correspond to time_steps.
This would mean that each conv kernel would use all time_steps and would then convolve over the days and features dimensions using its spatial size (in the default setup).
I.e. since you would like to get a single output channel, you could use e.g.:
x = torch.randn(5, 9, 30, 10)
conv = nn.Conv2d(9, 1, 3, 1, 1)
out = conv(x)
print(out.shape)
> torch.Size([5, 1, 30, 10])

which would use a single kernel with in_channel=9, a spatial kernel size of 3x3, a stride of 1, and padding of 1 to keep the “spatial size” of the output equal.