code: def enforce_output_layout(gm: torch.fx.GraphModule):
      """
      Make sure the output node's layout does not change due to compiler optimizations
      by adding aten.as_strided nodes with the expected strides.
  
      Only used for inference so we can assume all graph outputs are model outputs.
      """
      *_, output_node = gm.graph.nodes
      out_list = output_node.args[0]
      with gm.graph.inserting_before(output_node):
          for n in out_list:
              if not isinstance(
                  n.meta["val"], torch.Tensor
              ) or not torch._prims_common.is_non_overlapping_and_dense(
                  n.meta["val"]
              ):
                  continue
  
              # add a node to enforce eager layout
              ft = n.meta["val"]
              new_node = gm.graph.call_function(
                  prims.inductor_force_stride_order.default,
                  (n, ft.stride()),
              )
  
              # can not call
              # n.replace_all_uses_with(new_node)
              # since it will replace the usage of n in new_node itself.
              output_node.replace_input_with(n, new_node)
  
      gm.graph.lint()
      gm.recompile()
  
imports: from __future__ import annotations
  import itertools
  import logging
  import weakref
  from typing import Any, List, Optional, Tuple
  import torch
  import torch.utils._pytree
  from torch._dynamo.utils import dynamo_timed, lazy_format_graph_code
  from torch._functorch.aot_autograd import MutationType
  from torch._functorch.compile_utils import fx_graph_cse
  from torch._inductor.constant_folding import constant_fold, replace_node_with_constant
  from torch._inductor.fx_passes.freezing_patterns import freezing_passes
  from torch._inductor.fx_passes.post_grad import view_to_reshape
  from .None import config
label_desc: Use image node_id 5 for calling a global function with example usage: enforce_output_layout(gm) without return types