You can set the input in shape like (3,4), and use Conv1D to do the convolution with kernel in shape like (3,4). 1 dimension will be computed for one time to get one output, and you will get three features finally. And it won’t share the weight because of the kernel shape is (3,4) when computing.
Proccessing like the pic as follow

example.png947×322 2.89 KB