code: def test_frontend_method(
      *,
      init_input_dtypes: Union[ivy.Dtype, List[ivy.Dtype]] = None,
      method_input_dtypes: Union[ivy.Dtype, List[ivy.Dtype]],
      init_flags,
      method_flags,
      init_all_as_kwargs_np: Optional[dict] = None,
      method_all_as_kwargs_np: dict,
      frontend: str,
      frontend_method_data: FrontendMethodData,
      backend_to_test: str,
      on_device,
      rtol_: Optional[float] = None,
      atol_: float = 1e-06,
      tolerance_dict: Optional[dict] = None,
      test_values: Union[bool, str] = True,
  ):
      """Test a class-method that consumes (or returns) arrays for the current
      backend by comparing the result with numpy.
  
      Parameters
      ----------
      init_input_dtypes
          data types of the input arguments to the constructor in order.
      init_as_variable_flags
          dictates whether the corresponding input argument passed to the constructor
          should be treated as an ivy.Variable.
      init_num_positional_args
          number of input arguments that must be passed as positional arguments to the
          constructor.
      init_native_array_flags
          dictates whether the corresponding input argument passed to the constructor
          should be treated as a native array.
      init_all_as_kwargs_np:
          input arguments to the constructor as keyword arguments.
      method_input_dtypes
          data types of the input arguments to the method in order.
      method_all_as_kwargs_np:
          input arguments to the method as keyword arguments.
      frontend
          current frontend (framework).
      rtol_
          relative tolerance value.
      atol_
          absolute tolerance value.
      tolerance_dict
          dictionary of tolerance values for specific dtypes.
      test_values
          can be a bool or a string to indicate whether correctness of values should be
          tested. If the value is `with_v`, shapes are tested but not values.
  
      Returns
      -------
      ret
          optional, return value from the function
      ret_gt
          optional, return value from the Ground Truth function
      """
      # ToDo add with_backend refactor in GC
      _switch_backend_context(method_flags.test_trace)
  
      # Constructor arguments #
      (
          args_np_constructor,
          kwargs_np_constructor,
      ) = kwargs_to_args_n_kwargs(
          num_positional_args=init_flags.num_positional_args,
          kwargs=init_all_as_kwargs_np,
      )
  
      # extract all arrays from the arguments and keyword arguments
      (
          con_arg_np_vals,
          con_args_idxs,
          con_c_arg_vals,
      ) = _get_nested_np_arrays(args_np_constructor)
      (
          con_kwarg_np_vals,
          con_kwargs_idxs,
          con_c_kwarg_vals,
      ) = _get_nested_np_arrays(kwargs_np_constructor)
  
      # make all lists equal in length
      num_arrays_constructor = con_c_arg_vals + con_c_kwarg_vals
      if len(init_input_dtypes) < num_arrays_constructor:
          init_input_dtypes = [
              init_input_dtypes[0]
              for _ in range(num_arrays_constructor)
          ]
      if len(init_flags.as_variable) < num_arrays_constructor:
          init_flags.as_variable = [
              init_flags.as_variable[0]
              for _ in range(num_arrays_constructor)
          ]
      if len(init_flags.native_arrays) < num_arrays_constructor:
          init_flags.native_arrays = [
              init_flags.native_arrays[0]
              for _ in range(num_arrays_constructor)
          ]
  
      # update variable flags to be compatible with float dtype
      with BackendHandler.update_backend(
          backend_to_test
      ) as ivy_backend:
          init_flags.as_variable = [
              v if ivy_backend.is_float_dtype(d) else False
              for v, d in zip(init_flags.as_variable, init_input_dtypes)
          ]
  
      # Create Args
      args_constructor, kwargs_constructor = create_args_kwargs(
          backend=backend_to_test,
          args_np=args_np_constructor,
          arg_np_vals=con_arg_np_vals,
          args_idxs=con_args_idxs,
          kwargs_np=kwargs_np_constructor,
          kwarg_np_vals=con_kwarg_np_vals,
          kwargs_idxs=con_kwargs_idxs,
          input_dtypes=init_input_dtypes,
          test_flags=init_flags,
          on_device=on_device,
      )
      # End constructor #
  
      # Method arguments #
      args_np_method, kwargs_np_method = kwargs_to_args_n_kwargs(
          num_positional_args=method_flags.num_positional_args,
          kwargs=method_all_as_kwargs_np,
      )
  
      # extract all arrays from the arguments and keyword arguments
      (
          met_arg_np_vals,
          met_args_idxs,
          met_c_arg_vals,
      ) = _get_nested_np_arrays(args_np_method)
      (
          met_kwarg_np_vals,
          met_kwargs_idxs,
          met_c_kwarg_vals,
      ) = _get_nested_np_arrays(kwargs_np_method)
  
      # make all lists equal in length
      num_arrays_method = met_c_arg_vals + met_c_kwarg_vals
      if len(method_input_dtypes) < num_arrays_method:
          method_input_dtypes = [
              method_input_dtypes[0] for _ in range(num_arrays_method)
          ]
      if len(method_flags.as_variable) < num_arrays_method:
          method_flags.as_variable = [
              method_flags.as_variable[0]
              for _ in range(num_arrays_method)
          ]
      if len(method_flags.native_arrays) < num_arrays_method:
          method_flags.native_arrays = [
              method_flags.native_arrays[0]
              for _ in range(num_arrays_method)
          ]
  
      with BackendHandler.update_backend(
          backend_to_test
      ) as ivy_backend:
          if frontend == "jax":
              importlib.import_module(
                  "ivy.functional.frontends.jax"
              ).config.update("jax_enable_x64", True)
  
          method_flags.as_variable = [
              v if ivy_backend.is_float_dtype(d) else False
              for v, d in zip(
                  method_flags.as_variable, method_input_dtypes
              )
          ]
  
          # Create Args
          args_method, kwargs_method = create_args_kwargs(
              backend=backend_to_test,
              args_np=args_np_method,
              arg_np_vals=met_arg_np_vals,
              args_idxs=met_args_idxs,
              kwargs_np=kwargs_np_method,
              kwarg_np_vals=met_kwarg_np_vals,
              kwargs_idxs=met_kwargs_idxs,
              input_dtypes=method_input_dtypes,
              test_flags=method_flags,
              on_device=on_device,
          )
          # End Method #
          local_importer = ivy_backend.utils.dynamic_import
  
          create_frontend_array = local_importer.import_module(
              f"ivy.functional.frontends.{frontend}"
          )._frontend_array
  
          (
              args_constructor_ivy,
              kwargs_constructor_ivy,
          ) = ivy_backend.args_to_ivy(
              *args_constructor, **kwargs_constructor
          )
          args_method_ivy, kwargs_method_ivy = ivy_backend.args_to_ivy(
              *args_method, **kwargs_method
          )
  
          args_constructor_np = ivy_backend.nested_map(
              lambda x: (
                  ivy_backend.to_numpy(x._data)
                  if isinstance(x, ivy_backend.Array)
                  else x
              ),
              args_constructor_ivy,
              shallow=False,
          )
          kwargs_constructor_np = ivy_backend.nested_map(
              lambda x: (
                  ivy_backend.to_numpy(x._data)
                  if isinstance(x, ivy_backend.Array)
                  else x
              ),
              kwargs_constructor_ivy,
              shallow=False,
          )
          args_method_np = ivy_backend.nested_map(
              lambda x: (
                  ivy_backend.to_numpy(x._data)
                  if isinstance(x, ivy_backend.Array)
                  else x
              ),
              args_method_ivy,
              shallow=False,
          )
          kwargs_method_np = ivy_backend.nested_map(
              lambda x: (
                  ivy_backend.to_numpy(x._data)
                  if isinstance(x, ivy_backend.Array)
                  else x
              ),
              kwargs_method_ivy,
              shallow=False,
          )
  
          frontend_fw_module = (
              ivy_backend.utils.dynamic_import.import_module(
                  frontend_method_data.ivy_init_module
              )
          )
          ivy_frontend_creation_fn = getattr(
              frontend_fw_module, frontend_method_data.init_name
          )
  
          ins = ivy_frontend_creation_fn(
              *args_constructor, **kwargs_constructor
          )
          # If we have inplace operations, we need to create the frontend arrays
          # before running the method, so that after running the method
          # We can check whether the return is still an instance of the frontend array
          if method_flags.inplace:
              # make copies of the args and kwargs
              copy_args_method = copy.deepcopy(args_method)
              copy_kwargs_method = copy.deepcopy(kwargs_method)
              copy_ins = ivy_frontend_creation_fn(
                  *args_constructor, **kwargs_constructor
              )
              frontend_ret_ins = copy_ins.__getattribute__(
                  frontend_method_data.method_name
              )(*copy_args_method, **copy_kwargs_method)
              assert frontend_ret_ins is copy_ins, (
                  "Inplace method did not return the same instance of the"
                  f" frontend array, expected {copy_ins}, got {frontend_ret_ins}"
              )
          ret = get_frontend_ret(
              backend_to_test,
              ins.__getattribute__(frontend_method_data.method_name),
              *args_method_ivy,
              frontend_array_function=(
                  create_frontend_array
                  if method_flags.test_trace
                  else None
              ),
              test_trace=method_flags.test_trace,
              precision_mode=method_flags.precision_mode,
              **kwargs_method_ivy,
          )
  
          # test if return is frontend
          _assert_frontend_ret(ret, for_fn=False)
  
          ret_np_flat = flatten_frontend_to_np(
              ret=ret,
              backend=backend_to_test,
          )
  
      # Compute the return with the native frontend framework
      frontend_config = get_frontend_config(frontend)
      args_constructor_frontend = ivy.nested_map(
          lambda x: frontend_config.native_array(x)
          if isinstance(x, np.ndarray)
          else x,
          args_constructor_np,
          shallow=False,
      )
      kwargs_constructor_frontend = ivy.nested_map(
          lambda x: frontend_config.native_array(x)
          if isinstance(x, np.ndarray)
          else x,
          kwargs_constructor_np,
          shallow=False,
      )
      args_method_frontend = ivy.nested_map(
          lambda x: (
              frontend_config.native_array(x)
              if isinstance(x, np.ndarray)
              else (
                  frontend_config.as_native_dtype(x)
                  if isinstance(x, frontend_config.Dtype)
                  else (
                      frontend_config.as_native_device(x)
                      if isinstance(x, frontend_config.Device)
                      else x
                  )
              )
          ),
          args_method_np,
          shallow=False,
      )
      kwargs_method_frontend = ivy.nested_map(
          lambda x: frontend_config.native_array(x)
          if isinstance(x, np.ndarray)
          else x,
          kwargs_method_np,
          shallow=False,
      )
  
      # change ivy dtypes to native dtypes
      if "dtype" in kwargs_method_frontend:
          kwargs_method_frontend[
              "dtype"
          ] = frontend_config.as_native_dtype(
              kwargs_method_frontend["dtype"]
          )
  
      # change ivy device to native devices
      if "device" in kwargs_method_frontend:
          kwargs_method_frontend[
              "device"
          ] = frontend_config.as_native_device(
              kwargs_method_frontend["device"]
          )
      frontend_creation_fn = getattr(
          importlib.import_module(
              frontend_method_data.framework_init_module
          ),
          frontend_method_data.init_name,
      )
      ins_gt = frontend_creation_fn(
          *args_constructor_frontend, **kwargs_constructor_frontend
      )
      frontend_ret = ins_gt.__getattribute__(
          frontend_method_data.method_name
      )(*args_method_frontend, **kwargs_method_frontend)
      if frontend == "tensorflow" and isinstance(
          frontend_ret, tf.TensorShape
      ):
          frontend_ret_np_flat = [
              np.asarray(frontend_ret, dtype=np.int32)
          ]
      else:
          frontend_ret_np_flat = flatten_frontend_fw_to_np(
              frontend_ret,
              frontend_config.isscalar,
              frontend_config.is_native_array,
              frontend_config.to_numpy,
          )
  
      # assuming value test will be handled manually in the test function
      if not test_values:
          return ret, frontend_ret
  
      # value test
      if isinstance(rtol_, dict):
          rtol_ = _get_framework_rtol(rtol_, backend_to_test)
      if isinstance(atol_, dict):
          atol_ = _get_framework_atol(atol_, backend_to_test)
  
      value_test(
          ret_np_flat=ret_np_flat,
          ret_np_from_gt_flat=frontend_ret_np_flat,
          rtol=rtol_,
          atol=atol_,
          specific_tolerance_dict=tolerance_dict,
          backend=backend_to_test,
          ground_truth_backend=frontend,
      )
  
imports: import copy
  import time
  from typing import Union, List, Optional
  import numpy
  import types
  import importlib
  import inspect
  from collections import OrderedDict
  from .globals import mod_backend
  from .pipeline_helper import BackendHandler, BackendHandlerMode, get_frontend_config
  import ivy
  from ivy_tests.test_ivy.helpers.test_parameter_flags import FunctionTestFlags
  import ivy_tests.test_ivy.helpers.test_parameter_flags
  import ivy_tests.test_ivy.helpers.globals
  from ivy.functional.ivy.data_type import _get_function_list, _get_functions_from_string
  from ivy_tests.test_ivy.test_frontends import NativeClass
  from ivy_tests.test_ivy.helpers.structs import FrontendMethodData
  from ivy_tests.test_ivy.helpers.testing_helpers import _create_transpile_report
  from .assertions import value_test, assert_same_type, check_unsupported_dtype
label_desc: Use image node_id 16 for calling a global function with example usage: test_frontend_method() and returns: ret,  frontend_ret