code: def wrap_compiler_debug(unconfigured_compiler_fn, compiler_name: str):
      """
      Minifier for Fx Graph modules after Aot Autograd has finished. We wrap both
      forward and backward call separately with the backend compiler_fn - like
      inductor or nvfuser. Intercepting after Aot Autograd presents neat
      abstraction, where all the params are lifted as graph inputs, making it easy
      to save the graph as a string.
      """
  
      @functools.wraps(unconfigured_compiler_fn)
      def debug_wrapper(gm, example_inputs, **kwargs):
          from torch._subclasses import FakeTensorMode
  
          compiler_fn = functools.partial(
              unconfigured_compiler_fn, **kwargs
          )
  
          from torch._functorch.aot_autograd import get_aot_graph_name
  
          graph_name = get_aot_graph_name()
  
          # TODO: why do we need to deepcopy the original graph?
          orig_graph = copy.deepcopy(gm.graph)
          assert config.repro_after in ("dynamo", "aot", None)
  
          try:
              # Call the compiler_fn - which is either aot_autograd or inductor
              # with fake inputs
              inner_compiled_fn = compiler_fn(gm, example_inputs)
          except Exception as e:
              # TODO: Failures here are troublesome because no real inputs,
              # need a different serialization strategy
              if config.repro_after == "aot":
                  if config.repro_level == 1:
                      dump_compiler_graph_state(
                          fx.GraphModule(gm, orig_graph),
                          example_inputs,
                          compiler_name,
                      )
                  elif config.repro_level == 2:
                      dump_to_minify(
                          fx.GraphModule(gm, orig_graph),
                          example_inputs,
                          compiler_name,
                      )
                  log.error("CompilerError")
              raise
  
          # We may run regular PyTorch compute that may trigger Dynamo, do NOT
          # recursively attempt to accuracy minify in that case!
          def deferred_for_real_inputs(real_inputs):
              # This is a bit obscure: if we recursively try to accuracy minify
              # the SAME function, this would trigger.  But most of the time
              # we should never hit this branch
              if config.repro_after != "aot":
                  return inner_compiled_fn(real_inputs)
              with config.patch(repro_after=None):
                  return inner_debug_fn(real_inputs)
  
          def inner_debug_fn(real_inputs):
              """
              Aot Autograd fw_compiler and bw_compiler can have fake tensors. So,
              example_inputs can be fake tensors. We can call compiler_fn (which is
              inductor or nvfuser) with fake tensors but the actually compiled_fn
              should be called with real tensors. Therefore, the actual invocation
              is deferred.
              """
              # Copy the tensor attrs like shape, stride etc by converting to Fake Tensor
              # because inductor clears the tensor list in its codegen. And example_inputs
              # are available only for the first invocation.
              fake_mode = FakeTensorMode()
              copy_tensor_attrs = [
                  fake_mode.from_tensor(x)
                  if isinstance(x, torch.Tensor)
                  else x
                  for x in real_inputs
              ]
              if config.repro_level == 3:
                  # Always dump the original module in case we have segfaults
                  dump_to_minify(
                      fx.GraphModule(gm, orig_graph),
                      real_inputs,
                      compiler_name,
                  )
  
              if config.repro_level == 4:
                  if compiler_name != "inductor":
                      raise NotImplementedError(
                          "Accuracy minification is supported for inductor only"
                      )
                  if backend_aot_accuracy_fails(
                      gm, real_inputs, compiler_fn
                  ):
                      log.warning(
                          "Accuracy failed for the AOT Autograd graph %s",
                          graph_name,
                      )
                      dump_compiler_graph_state(
                          fx.GraphModule(gm, orig_graph),
                          real_inputs,
                          f"{compiler_name}_accuracy",
                      )
                      dump_to_minify(
                          fx.GraphModule(gm, orig_graph),
                          real_inputs,
                          f"{compiler_name}_accuracy",
                      )
                      raise AccuracyError("Bad accuracy detected")
                  else:
                      # Call the compiled function with real inputs
                      return inner_compiled_fn(real_inputs)
              else:
                  try:
                      # Call the compiled function with real inputs
                      out = inner_compiled_fn(real_inputs)
                      # sync cuda kernels to ensure IMA detection
                      for arg in example_inputs:
                          if (
                              isinstance(arg, torch.Tensor)
                              and arg.is_cuda
                          ):
                              torch.cuda.synchronize()
                              break
                      return out
                  except Exception as e:
                      if config.repro_level == 1:
                          dump_compiler_graph_state(
                              fx.GraphModule(gm, orig_graph),
                              copy_tensor_attrs,
                              compiler_name,
                          )
                      elif config.repro_level == 2:
                          dump_to_minify(
                              fx.GraphModule(gm, orig_graph),
                              copy_tensor_attrs,
                              compiler_name,
                          )
                      raise
  
          if config.repro_after == "aot":
              compiled_fn = deferred_for_real_inputs
              compiled_fn._boxed_call = True  # type: ignore[attr-defined]
              return compiled_fn
          else:
              return inner_compiled_fn
  
      return debug_wrapper
  
imports: import argparse
  import copy
  import functools
  import io
  import logging
  import os
  import shutil
  import subprocess
  import sys
  import textwrap
  import uuid
  from importlib import import_module
  from tempfile import TemporaryFile
  from typing import Any, Callable, Dict, Union
  import torch
  import torch.fx
  import torch.nn
  from torch._dynamo.debug_utils import _cuda_system_info_comment, AccuracyError, backend_accuracy_fails, BuckTargetWriter, cast_to_fp64, extra_imports, generate_config_string, helper_for_dump_minify, InputReader, InputWriter, MAX_CONSTANT_NUMEL_INLINE, minifier_dir, NNModuleToString, NopInputReader, same_two_models
  from torch._dynamo.utils import clone_inputs, counters, same
  from torch.fx.experimental.proxy_tensor import make_fx
  from torch.fx.experimental.symbolic_shapes import fx_placeholder_targets, has_free_symbols
  from torch.hub import tqdm
  from ..None import config
label_desc: Use image node_id 1 for calling a global function with example usage: wrap_compiler_debug(unconfigured_compiler_fn, compiler_name) and returns: debug_wrapper, compiled_fn, inner_compiled_fn, inner_compiled_fn, inner_debug_fn, inner_compiled_fn, out