code: def test_from_dlpack_shape_one():
      # A test case for the issue https://github.com/pytorch/pytorch/issues/99803
      import torch
      from torch.utils.dlpack import to_dlpack
  
      tgt = tvm.target.Target(target="llvm", host="llvm")
  
      rows = 1
      a = tvm.runtime.ndarray.from_dlpack(
          to_dlpack(torch.randn(rows, 16))
      )
  
      A = te.placeholder((rows, 16), name="A")
      B = te.placeholder((rows, 16), name="B")
      C = te.compute(A.shape, lambda i, j: A[i, j] + B[i, j], name="C")
  
      s = te.create_schedule(C.op)
  
      fadd = tvm.build(s, [A, B, C], tgt)
  
      dev = tvm.device(tgt.kind.name, 0)
  
      b = tvm.nd.array(
          np.random.uniform(size=(rows, 16)).astype(B.dtype), dev
      )
      c = tvm.nd.array(np.zeros((rows, 16), dtype=C.dtype), dev)
      fadd(a, b, c)
  
      tvm.testing.assert_allclose(c.numpy(), a.numpy() + b.numpy())
  
imports: import tvm
  import tvm.testing
  from tvm import te
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: test_from_dlpack_shape_one() without return types