code: def test_dot():
      """Test dot product."""
      arr_length = 12
      arr_length_tvm = tvm.runtime.convert(arr_length)
      placeholder_a = te.placeholder((arr_length_tvm,), name="A")
      placeholder_b = te.placeholder((arr_length_tvm,), name="B")
      reduce_axis_k = te.reduce_axis((0, arr_length_tvm), "k")
      result_c = te.compute(
          (),
          lambda: te.sum(
              placeholder_a[reduce_axis_k]
              * placeholder_b[reduce_axis_k],
              axis=reduce_axis_k,
          ),
          name="C",
      )
      schedule = te.create_schedule(result_c.op)
  
      def verify(target):
          f = tvm.driver.build(
              schedule, [placeholder_a, placeholder_b, result_c], target
          )
          # verify
          dev = tvm.cpu(0)
          buff_a = tvm.nd.array(
              np.random.uniform(size=(arr_length,)).astype(
                  placeholder_a.dtype
              ),
              dev,
          )
          buff_b = tvm.nd.array(
              np.random.uniform(size=(arr_length,)).astype(
                  placeholder_b.dtype
              ),
              dev,
          )
          buff_c = tvm.nd.array(np.zeros((), dtype=result_c.dtype), dev)
          f(buff_a, buff_b, buff_c)
          tvm.testing.assert_allclose(
              buff_c.numpy(),
              np.dot(buff_a.numpy(), buff_b.numpy()),
              rtol=1e-4,
          )
  
      verify("llvm")
  
imports: import numpy
  import tvm
  import tvm.testing
  from tvm import te
label_desc: Use image node_id 1 for calling a global function with example usage: test_dot() without return types