It depends a bit how you would like to process this input.
Currently your input has a shape of [3, 4], which is invalid for nn.Conv1d as well as nn.Conv2d.
If you want to use these two dimensions as the “spatial size”, i.e. similar to an input image, you would have to unsqueeze the batch and channel dimensions as:
input = input[None, None]
print(input.shape)
> torch.Size([1, 1, 3, 4])

Now you could use an nn.Conv2d layer with in_channels=1 and a kernel_size, which would have the same size or which would be smaller than the padded input of 3x4.
Note that you could also use one dimension as the channel dimension, which would then change the conv layer as well as the unsqueezing, so let me know what you would like to achieve.