Linear layer weights are of shape out_feat x in_feat, conv weights are out_chan x in_chan x kernel_height x kernel_width, so all you need is to use channels as features and then add two dimensions to the weight:
with torch.no_grad():
  conv_layer.weight.copy_(lin_layer[:, :, None, None])

Indexing with None adds a dimension with size 1.
Best regards
Thomas
Best regards
Thomas