code: def generate_repro(
      test: str,
      op: torch._ops.OpOverload,
      args: Tuple[Any, ...],
      kwargs: Dict[str, Any],
      *,
      save_data: bool,
      dry_run: bool = False,
  ) -> str:
      if save_data:
          now = datetime.datetime.now()
          path = os.path.join(
              tempfile.gettempdir(), "pytorch_opcheck_safe_to_delete"
          )
          unix_timestamp = datetime.datetime.timestamp(now) * 100000
          filepath = os.path.join(path, f"repro_{unix_timestamp}.pt")
          if not dry_run:
              os.makedirs(path, exist_ok=True)
              torch.save((args, kwargs), filepath)
          args_kwargs = f'args, kwargs = torch.load("{filepath}")'
      else:
          args_kwargs = (
              "# If you rerun your test with PYTORCH_OPCHECK_PRINT_BETTER_REPRO=1\n"
              "# we will fill them in same (args, kwargs) as in your test\n"
              "args = ()  # args to the operator\n"
              "kwargs = {}  # kwargs to the operator"
          )
  
      ns, name = op._schema.name.split("::")
      overload = op._overloadname
  
      repro_command = (
          f"# =========================================================\n"
          f"# BEGIN REPRO SCRIPT\n"
          f"# =========================================================\n"
          f"import torch\n"
          f"from torch.testing._internal.optests import opcheck\n"
          f"\n"
          f"# Make sure you have loaded the library that contains the op\n"
          f"# via an import or torch.ops.load_library(...)\n"
          f"op = torch.ops.{ns}.{name}.{overload}\n"
          f"\n"
          f"{args_kwargs}\n"
          f'opcheck(op, args, kwargs, test_utils="{test}")\n'
          f"# =========================================================\n"
          f"# END REPRO SCRIPT\n"
          f"# =========================================================\n"
      )
      return repro_command
  
imports: import datetime
  import difflib
  import functools
  import inspect
  import json
  import os
  import re
  import tempfile
  import threading
  import unittest
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import torch
  import torch._dynamo
  import torch.utils._pytree
  from torch._dynamo.utils import clone_input
  from torch._subclasses.schema_check_mode import SchemaCheckMode
  from torch._utils_internal import get_file_path_2
  from torch.overrides import TorchFunctionMode
  from torch.testing._internal.optests import aot_autograd_check, autograd_registration_check, fake_check
label_desc: Use image node_id 16 for calling a global function with example usage: generate_repro(test, op, args, kwargs) and returns: repro_command