code: def out_variant(
      self,
      groups: Sequence[NativeFunctionsGroup],
      backend_index: BackendIndex,
  ) -> str:
      if not groups:
          return ""
      generated_type_variants = []
      for g in groups:
          with native_function_manager(g):
              assert is_supported(g)
              assert isinstance(g, NativeFunctionsGroup)
              generated_type_variant = self.out_variant_op_generator(
                  g, backend_index
              )
              generated_type_variants.append(generated_type_variant)
      op_name = op_name_from_group(groups[0])
      body = "\n".join(generated_type_variants)
      generated = f"""
  REGISTER_OPERATOR_FUNCTOR(
      aten::{op_name},
      aten_{op_name},
      [](Node* n) -> SROperator {{
        {body}
        LogAndDumpSchema(n);
        return nullptr;
      }});
  """
      return generated
  
imports: import json
  import logging
  import math
  from typing import Dict, List, Optional, Sequence, Tuple, Union
  import torchgen.api.cpp
  from torchgen.context import native_function_manager
  from torchgen.model import Argument, BackendIndex, BaseTy, BaseType, FunctionSchema, NativeFunctionsGroup, NativeFunctionsViewGroup, OptionalType, SelfArgument, TensorOptionsArguments, Type
  from torchgen.static_runtime import config
label_desc: Use image node_id 1 for calling the GenOpDispatcher obj's underlying member method code with example usage: obj.out_variant(groups, backend_index) and returns: generated, str