code: def verify_slice_like(
      executor_kind, data, slice_like, axes, output, dtype="float32"
  ):
      x = relay.var("data", relay.TensorType(data, dtype))
      y = relay.var("slice_like", relay.TensorType(slice_like, dtype))
      z = relay.slice_like(x, y, axes)
      zz = run_infer_type(z)
      if axes:
          assert "axes" in z.astext()
      assert zz.checked_type == relay.ty.TensorType(output, dtype)
  
      if all(isinstance(v, int) == 0 for v in data) or all(
          isinstance(v, int) == 0 for v in slice_like
      ):
          return
  
      func = relay.Function([x, y], z)
      x_data = np.random.uniform(size=data).astype(dtype)
      y_data = np.random.uniform(size=slice_like).astype(dtype)
      ref_res = np_slice_like(x_data, y_data, axes)
  
      for target, dev in tvm.testing.enabled_targets():
          op_res = relay.create_executor(
              executor_kind, device=dev, target=target
          ).evaluate(func)(x_data, y_data)
          tvm.testing.assert_allclose(
              op_res.numpy(), ref_res, rtol=1e-5
          )
  
imports: import sys
  import pytest
  import numpy
  import tvm
  import tvm.testing
  import tvm.topi.testing
  from tvm import relay, te, topi
  from tvm.relay import transform
  from tvm.relay.testing import run_infer_type
label_desc: Use image node_id 12 for calling a global function with example usage: verify_slice_like(executor_kind, data, slice_like, axes, output, dtype) without return types