code: def test_inject_ptx_intrin():
      f = vector_add
      arch = tvm.contrib.nvcc.get_target_compute_version()
      major, _ = tvm.contrib.nvcc.parse_compute_version(arch)
      if major < 8:
          # Require at least SM80
          return
      with tvm.transform.PassContext(config={"tir.ptx_ldg32": True}):
          mod = tvm.build(f, target="cuda")
      A_np = np.random.rand(16).astype("float32")
      B_np = np.zeros((32)).astype("float32")
      dev = tvm.cuda(0)
      A_nd = tvm.nd.array(A_np, device=dev)
      B_nd = tvm.nd.array(B_np, device=dev)
      mod(A_nd, B_nd)
  
      C_np = np.zeros((32)).astype("float32")
  
      for i in range(32):
          if i % 2 == 0:
              C_np[i] = A_np[i // 2]
          C_np[i] += 1.0
  
      tvm.testing.assert_allclose(B_nd.numpy(), C_np)
  
imports: import tvm
  from tvm.script import tir
  import numpy
  import tvm.testing
label_desc: Use image node_id 2 for calling a global function with example usage: test_inject_ptx_intrin() without return types