code: def _vjp_with_argnums(
      func: Callable,
      *primals,
      argnums: Optional[argnums_t] = None,
      has_aux: bool = False,
  ):
      # This is the same function as vjp but also accepts an argnums argument
      # All args are the same as vjp except for the added argument
      # argnums (Optional[int or tuple[int]]): Optional, specifies the argument(s) to compute gradients with respect to.
      #         If None, computes the gradients with respect to all inputs (used for vjp). Default: None
      #
      # WARN: Users should NOT call this function directly and should just be calling vjp.
      # It is only separated so that inputs passed to jacrev but not differentiated get the correct wrappers.
      #
      # NOTE: All error messages are produced as if vjp was being called, even if this was called by jacrev
      #
      # Returns the same two elements as :func:`vjp` but the function returned, vjp_fn, returns a tuple of VJPs
      # for only the primal elements given by argnums.
      level = _grad_increment_nesting()
      try:
          # See NOTE [grad and vjp interaction with no_grad]
          with torch.enable_grad():
              primals = _wrap_all_tensors(primals, level)
              if argnums is None:
                  diff_primals = _create_differentiable(primals, level)
              else:
                  diff_primals = _slice_argnums(
                      primals, argnums, as_tuple=False
                  )
                  tree_map_(
                      partial(_create_differentiable, level=level),
                      diff_primals,
                  )
              primals_out = func(*primals)
  
              if has_aux:
                  if not (
                      isinstance(primals_out, tuple)
                      and len(primals_out) == 2
                  ):
                      raise RuntimeError(
                          "vjp(f, *primals): output of function f should be a tuple: (output, aux) "
                          "if has_aux is True"
                      )
                  primals_out, aux = primals_out
                  aux = _undo_create_differentiable(aux, level)
  
              flat_primals_out, primals_out_spec = tree_flatten(
                  primals_out
              )
              assert_non_empty_tensor_output(
                  flat_primals_out, "vjp(f, *primals)"
              )
              flat_diff_primals, primals_spec = tree_flatten(
                  diff_primals
              )
              results = _undo_create_differentiable(primals_out, level)
  
              for primal_out in flat_primals_out:
                  assert isinstance(primal_out, torch.Tensor)
                  if (
                      primal_out.is_floating_point()
                      or primal_out.is_complex()
                  ):
                      continue
                  raise RuntimeError(
                      "vjp(f, ...): All outputs of f must be "
                      "floating-point or complex Tensors, got Tensor "
                      f"with dtype {primal_out.dtype}"
                  )
  
          def wrapper(cotangents, retain_graph=True, create_graph=None):
              if create_graph is None:
                  create_graph = torch.is_grad_enabled()
              flat_cotangents, cotangents_spec = tree_flatten(
                  cotangents
              )
              if primals_out_spec != cotangents_spec:
                  raise RuntimeError(
                      f"Expected pytree structure of cotangents to be the same "
                      f"as pytree structure of outputs to the function. "
                      f"cotangents: {treespec_pprint(cotangents_spec)}, "
                      f"primal output: {treespec_pprint(primals_out_spec)}"
                  )
              result = _autograd_grad(
                  flat_primals_out,
                  flat_diff_primals,
                  flat_cotangents,
                  retain_graph=retain_graph,
                  create_graph=create_graph,
              )
              return tree_unflatten(result, primals_spec)
  
      finally:
          _grad_decrement_nesting()
  
      if has_aux:
          return results, wrapper, aux
      else:
          return results, wrapper
  
imports: from typing import Callable, Union, Tuple, List, Any, Optional
  import torch
  from functools import partial, wraps
  import contextlib
  from torch.utils._pytree import tree_flatten, tree_unflatten, tree_map, tree_map_only, tree_map_, treespec_pprint
  from torch.utils import _pytree
  from torch.fx.experimental import const_fold
  from torch.fx.experimental.proxy_tensor import make_fx
  import torch.autograd.forward_ad
  from torch._subclasses.functional_tensor import FunctionalTensor
  from .vmap import doesnt_support_saved_tensors_hooks, get_chunk_sizes
  from .apis import vmap
  from torch._C._functorch import _wrap_for_grad, _unwrap_for_grad, _grad_increment_nesting, _grad_decrement_nesting, _jvp_increment_nesting, _jvp_decrement_nesting, _wrap_functional_tensor, _unwrap_functional_tensor, _func_decrement_nesting, _func_increment_nesting, _assert_wrapped_functional, _propagate_functional_input_mutation, set_inplace_requires_grad_allowed, get_inplace_requires_grad_allowed
  from torch._functorch.utils import exposed_in, argnums_t
label_desc: Use image node_id 12 for calling a global function with example usage: _vjp_with_argnums(func) and returns: results,  wrapper,  aux, results,  wrapper, tree_unflatten