code: def test_dtype(
      *,
      array,
      input_dtype,
      as_native,
      test_flags,
      backend_fw,
      fn_name,
      on_device,
  ):
      if backend_fw == "torch":
          if input_dtype == "bfloat16" or (
              "uint" in input_dtype and "uint8" not in input_dtype
          ):
              # Torch has no inference strategy for bfloat16
              # Torch has no support for uint above uint8
              return
  
      helpers.test_function(
          input_dtypes=input_dtype,
          test_flags=test_flags,
          backend_to_test=backend_fw,
          fn_name=fn_name,
          on_device=on_device,
          x=array,
          as_native=as_native,
          test_values=False,
      )
  
imports: import numpy
  from hypothesis import strategies
  import typing
  import ivy
  import ivy_tests.test_ivy.helpers
  from ivy_tests.test_ivy.helpers import handle_test, BackendHandler
label_desc: Use image node_id 19 for calling a global function with example usage: test_dtype() without return types