code: def verify_vectorization(n, m, dtype):
      def check_targeta(targeta):
          if not tvm.testing.device_enabled(targeta):
              print("Skip because %s is not enabled" % targeta)
              return
          if (
              dtype == "float16"
              and targeta == "cuda"
              and not have_fp16(tvm.cuda(0).compute_version)
          ):
              print("Skip because gpu does not have fp16 support")
              return
          with tvm.target.Target(targeta):
              dev = tvm.device(targeta, 0)
              A = te.placeholder((n, m), name="A", dtype=dtype)
              B = te.compute(
                  (n, m),
                  lambda i, j: A[i, j] + tvm.tir.const(1, A.dtype),
                  name="B",
              )
              S = tvm.topi.testing.get_elemwise_schedule(targeta)(B)
  
              fun = tvm.build(S, [A, B], targeta)
              np_A = tvm.nd.empty((n, m), A.dtype, dev).copyfrom(
                  np.random.uniform(size=(n, m))
              )
              np_B = tvm.nd.empty((n, m), B.dtype, dev)
              fun(np_A, np_B)
              tvm.testing.assert_allclose(
                  np_B.numpy(), np_A.numpy() + 1, rtol=1e-5
              )
  
      for targeta in ["cuda"]:
          check_targeta(targeta)
  
file_path: tvm/tests/python/topi/test_topi_tensor.py
imports: import numpy
  import tvm
  from tvm import te
  from tvm import topi
  import tvm.topi.testing
  from tvm.contrib.pickle_memoize import memoize
  from tvm.contrib.nvcc import have_fp16
  import tvm.testing
label_desc: Use image node_id 3 for calling a global function with example usage: verify_vectorization(n, m, dtype) without return types