code: def test_array_multiplication():
      # 1. Create Kompute Manager (selects device 0 by default)
      mgr = kp.Manager()
  
      # 2. Create Kompute Tensors to hold data
      tensor_in_a = mgr.tensor(np.array([2, 2, 2]))
      tensor_in_b = mgr.tensor(np.array([1, 2, 3]))
      tensor_out = mgr.tensor(np.array([0, 0, 0]))
  
      params = [tensor_in_a, tensor_in_b, tensor_out]
  
      # 4. Define the multiplication shader code to run on the GPU
      @ps.python2shader
      def compute_mult(
          index=("input", "GlobalInvocationId", ps.ivec3),
          data1=("buffer", 0, ps.Array(ps.f32)),
          data2=("buffer", 1, ps.Array(ps.f32)),
          data3=("buffer", 2, ps.Array(ps.f32)),
      ):
          i = index.x
          data3[i] = data1[i] * data2[i]
  
      (
          mgr.sequence()
          .record(kp.OpTensorSyncDevice(params))
          .record(
              kp.OpAlgoDispatch(
                  mgr.algorithm(params, compute_mult.to_spirv())
              )
          )
          .record(kp.OpTensorSyncLocal([tensor_out]))
          .eval()
      )
  
      assert tensor_out.data().tolist() == [2.0, 4.0, 6.0]
      assert np.all(tensor_out.data() == [2.0, 4.0, 6.0])
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_array_multiplication() without return types