code: def __call__(
      self,
      unbox_kernel_entry: Tuple[
          NativeFunction, Tuple[ETKernelKey, BackendMetadata]
      ],
  ) -> str:
      f: NativeFunction = unbox_kernel_entry[0]
      kernel_key: Union[
          ETKernelKey, List[ETKernelKey]
      ] = unbox_kernel_entry[1][0]
      kernel_meta: BackendMetadata = unbox_kernel_entry[1][1]
  
      op_name = f"{f.namespace}::{f.func.name}"
      if not self.selector.is_root_operator(op_name):
          return ""
  
      if not isinstance(kernel_key, list):
          kernel_key = [kernel_key]
      used_kernel_keys = self.selector.et_get_selected_kernels(
          op_name, [k.to_native_string() for k in kernel_key]
      )
      if not used_kernel_keys:
          return ""
      sig: Union[CppSignature, ExecutorchCppSignature]
      argument_type_gen: Callable[..., NamedCType]
      return_type_gen: Callable[..., CType]
      if self.use_aten_lib:
          sig = CppSignatureGroup.from_native_function(
              f, method=False, fallback_binding=f.manual_cpp_binding
          ).most_faithful_signature()
          argument_type_gen = aten_cpp.argumenttype_type
          return_type_gen = aten_cpp.returns_type
          arguments = sig.arguments()
          kernel_call = f"torch::executor::{f.namespace}::{sig.name()}"
      else:
          sig = ExecutorchCppSignature.from_native_function(f)
          argument_type_gen = et_cpp.argumenttype_type
          return_type_gen = et_cpp.returns_type
          arguments = sig.arguments(include_context=False)
          kernel_call = (
              f"{kernel_meta.cpp_namespace}::{kernel_meta.kernel}"
          )
      # parse arguments into C++ code
      binding_list, code_list = Unboxing(
          argument_type_gen=argument_type_gen
      ).convert_arguments(arguments)
  
      # for each C++ argument, generate the conversion code
      code_connector = "\n\t"
      arg_connector = ", "
  
      args_str = f"{arg_connector.join(e.name for e in binding_list)}"
      event_tracer_output_logging = ""
      output_ids = []
  
      if len(f.func.returns) == 0:
          if len(f.func.arguments.out) == 0:
              raise Exception(
                  f"Can't handle native function {f.func} with no returns and no out yet."
              )
          out = f.func.arguments.out[0]
          return_assignment = (
              f"""stack[{len(binding_list)}] = &{out.name};"""
          )
          ret_prefix = ""
          output_ids = [len(binding_list)]
      else:
          if len(f.func.arguments.out) == 0:
              return_assignment = (
                  f"""*stack[{len(binding_list)}] = EValue(result_);"""
              )
              ret_prefix = (
                  return_type_gen(f.func.returns).cpp_type()
                  + " result_ = "
              )
              output_ids = [len(binding_list)]
          else:
              return_assignment = ""
              ret_prefix = ""
              output_ids = [
                  len(binding_list) - (i + 1)
                  for i in reversed(range(len(f.func.arguments.out)))
              ]
  
      for output_id in output_ids:
          event_tracer_output_logging += (
              f"internal::event_tracer_log_evalue("
              f"context.internal_event_tracer(), "
              f"*stack[{output_id}]);\n"
          )
  
      newline = "\n    "
      return "\n".join(
          [
              f"""
  Kernel(
      "{f.namespace}::{f.func.name}",{newline + '"' + (k + '",') if k != 'default' else ''}
      []({contextArg.defn()}, EValue** stack) {{
          {code_connector.join(code_list)}
  
          internal::EventTracerProfileScope event_tracer_scope(context.internal_event_tracer(), "native_call_{f.func.name}");
          EXECUTORCH_SCOPE_PROF("native_call_{f.func.name}");
          {ret_prefix}{kernel_call}(context, {args_str});
          {event_tracer_output_logging}
          {return_assignment}
      }}
  ),
  """
              for k in used_kernel_keys
          ]
      )
  
imports: import argparse
  import os
  import pathlib
  from collections import defaultdict
  from dataclasses import dataclass
  from typing import Any, Callable, Dict, List, Optional, Sequence, TextIO, Tuple, Union
  import yaml
  from torchgen import dest
  from torchgen.api import cpp
  from torchgen.api.types import CppSignature, CppSignatureGroup, CType, NamedCType
  from torchgen.context import method_with_native_function, method_with_nested_native_function, with_native_function_and_index
  from torchgen.executorch.api import et_cpp
  from torchgen.executorch.api.custom_ops import ComputeNativeFunctionStub, gen_custom_ops_registration
  from torchgen.executorch.api.types import contextArg, ExecutorchCppSignature
  from torchgen.executorch.api.unboxing import Unboxing
  from torchgen.executorch.model import ETKernelIndex, ETKernelKey, ETParsedYaml
  from torchgen.executorch.parse import ET_FIELDS, parse_et_yaml, parse_et_yaml_struct
  from torchgen.gen import get_custom_build_selector, get_native_function_declarations, get_native_function_declarations_from_ns_grouped_kernels, get_native_function_schema_registrations, LineLoader, parse_native_yaml
  from torchgen.model import BackendIndex, BackendMetadata, DEFAULT_KERNEL_NAMESPACE, DispatchKey, FunctionSchema, Location, NativeFunction, NativeFunctionsGroup, OperatorName, Variant
  from torchgen.selective_build.selector import SelectiveBuilder
  from torchgen.utils import context, FileManager, make_file_manager, mapMaybe, NamespaceHelper
label_desc: Use image node_id 1 for calling the ComputeCodegenUnboxedKernels obj's underlying member method code with example usage: obj.__call__(unbox_kernel_entry) and returns: str, str, str