code: def test_numpy_nanpercentile(
      dtype_values_axis,
      where,
      frontend,
      test_flags,
      fn_tree,
      backend_fw,
      on_device,
      keep_dims,
  ):
      input_dtypes, values, axis = dtype_values_axis
      if isinstance(axis, tuple):
          axis = axis[0]
  
      (
          where,
          input_dtypes,
          test_flags,
      ) = np_frontend_helpers.handle_where_and_array_bools(
          where=where,
          input_dtype=input_dtypes,
          test_flags=test_flags,
      )
  
      np_frontend_helpers.test_frontend_function(
          a=values[0][0],
          q=values[0][1],
          axis=axis,
          out=None,
          backend_to_test=backend_fw,
          overwrite_input=None,
          method=None,
          keepdims=keep_dims,
          interpolation=None,
          frontend=frontend,
          fn_tree=fn_tree,
          test_flags=test_flags,
          input_dtypes=input_dtypes,
      )
  
imports: from hypothesis import strategies
  from ivy_tests.test_ivy.test_functional.test_core.test_statistical import _statistical_dtype_values
  import ivy_tests.test_ivy.helpers
  import ivy_tests.test_ivy.test_frontends.test_numpy.helpers
  from ivy_tests.test_ivy.helpers import handle_frontend_test
label_desc: Use image node_id 1 for calling a global function with example usage: test_numpy_nanpercentile(dtype_values_axis, where, frontend, test_flags, fn_tree, backend_fw, on_device, keep_dims) without return types