code: def _get_setup(
      benchmark: GroupedBenchmark,
      runtime: RuntimeMode,
      language: Language,
      stmt: str,
      model_path: Optional[str],
  ) -> str:
      """Specialize a GroupedBenchmark for a particular configuration.
  
      Setup requires two extra pieces of information:
        1) The benchmark stmt. This is needed to warm up the model and avoid
           measuring lazy initialization.
        2) The model path so we can load it during the benchmark.
  
      These are only used when `runtime == RuntimeMode.JIT`.
      """
  
      # By the time we get here, details about how to set up a model have already
      # been determined by GroupedBenchmark. (Or set to None if appropriate.) We
      # simply need to collect and package the code blocks.
      if language == Language.PYTHON:
          setup = benchmark.setup.py_setup
          model_setup = benchmark.py_model_setup
      else:
          assert language == Language.CPP
          setup = benchmark.setup.cpp_setup
          model_setup = benchmark.cpp_model_setup
  
      if runtime == RuntimeMode.EAGER:
          return "\n".join([setup, model_setup or ""])
  
      assert runtime == RuntimeMode.JIT
      assert model_path is not None
  
      # We template `"{model_path}"`, so quotes would break model loading. The
      # model path is generated within the benchmark, so this is just an
      # abundance of caution rather than something that is expected in practice.
      assert '"' not in model_path
  
      # `stmt` may contain newlines, so we can't use f-strings. Instead we need
      # to generate templates so that dedent works properly.
      if language == Language.PYTHON:
          setup_template: str = textwrap.dedent(
              f"""
              jit_model = torch.jit.load("{model_path}")
  
              # Warmup `jit_model`
              for _ in range(3):
              {{stmt}}
          """
          )
  
      else:
          assert language == Language.CPP
          setup_template = textwrap.dedent(
              f"""
              const std::string fpath = "{model_path}";
              auto jit_model = torch::jit::load(fpath);
  
              // Warmup `jit_model`
              for (int i = 0; i < 3; i++) {{{{
              {{stmt}}
              }}}}
          """
          )
  
      model_load = setup_template.format(
          stmt=textwrap.indent(stmt, " " * 4)
      )
      return "\n".join([setup, model_load])
  
file_path: pytorch/benchmarks/instruction_counts/core/expand.py
imports: import importlib.abc
  import importlib.util
  import itertools
  import os
  import re
  import textwrap
  import uuid
  from typing import List, Optional, Tuple, TYPE_CHECKING
  import torch
  from core.api import AutogradMode, AutoLabels, GroupedBenchmark, RuntimeMode, TimerArgs
  from core.types import FlatDefinition, FlatIntermediateDefinition, Label
  from core.utils import get_temp_dir
label_desc: Use image node_id 3 for calling a global function with example usage: _get_setup(benchmark, runtime, language, stmt, model_path) and returns: str, str