code: def generate_single_level_function(interpreter, autograd_function):
      level = interpreter.level()
  
      def forward(*operands):
          unwrapped_operands = pytree.tree_map_only(
              torch.Tensor,
              lambda x: _unwrap_for_grad(x, level),
              operands,
          )
          # Both enable_grad() and _set_fwd_grad_enabled() are necessary no matter
          # the transform. _SingleLevelFunction will turn off both fwd and bwd
          # gradient computation and we need to turn it back on here.
          with torch.enable_grad(), _set_fwd_grad_enabled(
              True
          ), interpreter.lower():
              unwrapped_output = custom_function_call(
                  autograd_function, *unwrapped_operands
              )
  
          # See NOTE [mark_dirty object identity check]
          def wrap_fn(output):
              return _wrap_for_grad(output, level)
  
          return wrap_outputs_maintaining_identity(
              unwrapped_output, unwrapped_operands, operands, wrap_fn
          )
  
      def setup_context(ctx, inputs, output):
          return autograd_function.setup_context(ctx, inputs, output)
  
      # backward is only used if the transform is TransformType.Grad
      def backward(ctx, *grads):
          result = autograd_function.backward(ctx, *grads)
          return result
  
      # jvp is only used if the transform is TransformType.Jvp
      def jvp(ctx, *tangents):
          result = autograd_function.jvp(ctx, *tangents)
          return result
  
      # This is the sequence of magic words to dynamically generate a Subclass with
      # a given name. A Tensor's .grad_fn field has a class name that is the original
      # autograd.Function's name + Backward, so we do this to generate some
      # meaningful name.
      name = f"{autograd_function.__name__}Generated"
      Generated = type(
          name,
          (torch.autograd.function._SingleLevelFunction,),
          {
              "forward": staticmethod(forward),
              "backward": staticmethod(backward),
              "jvp": staticmethod(jvp),
              "setup_context": staticmethod(setup_context),
          },
      )
      return Generated
  
imports: import torch
  from torch._ops import HigherOrderOperator
  from torch._C._functorch import TransformType
  from torch._functorch.utils import enable_single_level_autograd_function
  import torch.utils._pytree
  from torch._C._functorch import _wrap_for_grad, _unwrap_for_grad, current_level
  from torch._functorch.vmap import wrap_batched, unwrap_batched, restore_vmap, _add_batch_dim
  from torch._functorch.apis import vmap
  from torch._functorch.vmap import _broadcast_to_and_flatten
  from torch.autograd.forward_ad import _set_fwd_grad_enabled
  from typing import Any, NamedTuple, Tuple
label_desc: Use image node_id 2 for calling a global function with example usage: generate_single_level_function(interpreter, autograd_function) and returns: Generated, wrap_outputs_maintaining_identity, autograd_function, result, result, _wrap_for_grad