code: def verify(model, args, loss_fn=torch.sum, devices=None):
      """
      Verify that a JIT compiled model has the same behavior as its uncompiled version along with its backwards pass.
  
      If your model returns multiple outputs,
      you must also specify a `loss_fn` to produce a loss for which
      the backwards will be computed.
  
      This function has side-effects (e.g., it executes your model / saves and loads
      parameters), so don't expect the model to come out exactly the same as what
      you passed in.
  
      Args:
          model (compiled torch.nn.Module or function): the module/function to be
              verified.  The module/function definition MUST have been decorated with
              `@torch.jit.compile`.
          args (tuple or Tensor): the positional arguments to pass to the
              compiled function/module to be verified.  A non-tuple is assumed to
              be a single positional argument to be passed to the model.
          loss_fn (function, optional): the loss function to be applied to
              the output of the model, before backwards is invoked.  By default,
              we assume that a model returns a single result, and we :func:`torch.sum`
              before calling backwards; if this is inappropriate, you can pass your
              own loss function.  Note that if a model returns a tuple of results,
              these are passed as separate positional arguments to `loss_fn`.
          devices (iterable of device IDs, optional): the GPU devices which the
              compiled module will be run on.  This determines the RNG state we
              must save when running both compiled and uncompiled versions of the model.
      """
      # TODO: In principle, we track device information in our trace, so it
      # should be possible to check if our execution actually obeyed the 'devices'
      # the user provided.
  
      # TODO: Consider adding a utility function to torch.jit to test
      # for this case
      if not isinstance(model, torch._C.CompiledFunction):  # type: ignore[attr-defined]
          raise TypeError(
              "Cannot verify an uncompiled module.  Add @torch.jit.compile to compile it"
          )
      is_module = isinstance(model, Module)
  
      if not isinstance(args, tuple):
          args = (args,)
  
      saved_args = _clone_inputs(args)
      if is_module:
          saved_state = copy.deepcopy(model.state_dict())
  
      def run_fwd_bwd(args, force_trace=False, assert_compiled=False):
          params = list(model.parameters()) if is_module else []
          in_vars, _ = _flatten((args, params))
          # We use a special API to reset the trace and compile it from scratch.
          compiled_fn = model
          if force_trace:
              compiled_fn.clear_cache()
          if assert_compiled:
              hits = compiled_fn.hits
          out = model(*args)
          if assert_compiled and compiled_fn.hits == hits:
              raise RuntimeError("failed to use the compiled function")
          if not isinstance(out, tuple):
              out = (out,)
          if loss_fn == torch.sum and len(out) != 1:
              raise ValueError(
                  f"Model returns {len(out)} outputs, but default loss function "
                  "(torch.sum) can only handle a single output"
              )
          out_vars, _ = _flatten(out)
          saved_outs = [
              v.detach().clone(memory_format=torch.preserve_format)
              for v in out_vars
          ]
          loss = loss_fn(*out)
          grads = torch.autograd.grad([loss], in_vars)
          # TODO: I'm not sure if the clone here is necessary but it is safer
          saved_grads = [
              v.detach().clone(memory_format=torch.preserve_format)
              for v in grads
          ]
          return (saved_outs, saved_grads)
  
      with torch.random.fork_rng(devices, _caller="torch.jit.verify"):
          uncompiled_outs, uncompiled_grads = run_fwd_bwd(
              args, force_trace=True
          )
          assert model.has_trace_for(*args)
  
      if is_module:
          model.load_state_dict(saved_state)
      compiled_outs, compiled_grads = run_fwd_bwd(
          args, assert_compiled=True
      )
  
      _verify_equal(uncompiled_outs, compiled_outs)
      _verify_equal(uncompiled_grads, compiled_grads)
  
imports: import contextlib
  import copy
  import functools
  import inspect
  import os
  import re
  import warnings
  from typing import Any, Callable, Dict, List, Optional, Set, TypeVar
  from typing_extensions import ParamSpec
  import torch
  from torch._jit_internal import _qualified_name, get_callable_argument_names, is_scripting
  from torch.autograd import function
  from torch.jit._script import _CachedForward, script, ScriptModule
  from torch.jit._state import _enabled, _python_cu
  from torch.nn import Module
  from torch.testing._comparison import default_tolerances
label_desc: Use image node_id 5 for calling a global function with example usage: verify(model, args, loss_fn, devices) and returns: saved_outs,  saved_grads