code: def test_numpy_min_scalar_type(
      *,
      x,
      on_device,
      fn_tree,
      frontend,
      test_flags,
      backend_fw,
  ):  # skip torch backend uint
      if ivy.current_backend_str() == "torch":
          assume(not isinstance(x, int))
      ret, frontend_ret = helpers.test_frontend_function(
          input_dtypes=[],
          backend_to_test=backend_fw,
          frontend=frontend,
          test_flags=test_flags,
          fn_tree=fn_tree,
          on_device=on_device,
          a=x,
          test_values=False,
      )
      assert ret._ivy_dtype == frontend_ret[0].name
  
imports: from hypothesis import strategies, settings, assume
  import ivy
  import ivy_tests.test_ivy.helpers
  from ivy_tests.test_ivy.helpers.testing_helpers import handle_frontend_test
label_desc: Use image node_id 2 for calling a global function with example usage: test_numpy_min_scalar_type() without return types