data and model parallelism in an easier manner.

    For example, the following:

    ```mlir
    !rtype = type tensor<!tf_type.resource<tensor<10x3xf32>>>
    func @data_and_model_parallelism(%arg0: !rtype, %arg1: !rtype, %arg2: !rtype, %arg3: !rtype) -> !rtype {

//                (tensor<4x2xf32>, tensor<4x2xf32>, tensor<4x2xf32>)
//
// will be converted into:
//
//   %0 = "mhlo.slice"(%input) {
//             limit_indices = dense<[4, 2]> : tensor<2xi64>,
//             start_indices = dense<0> : tensor<2xi64>,
//             strides = dense<1> : tensor<2xi64>} :
//        (tensor<4x6xf32>) -> tensor<4x2xf32>