code: def _slice_argnums(args, argnums, as_tuple=True):
      if not isinstance(argnums, int) and not isinstance(
          argnums, tuple
      ):
          raise RuntimeError(
              f"argnums must be int or Tuple[int, ...], got: {type(argnums)}"
          )
      argnums = _validate_and_wrap_argnums(argnums, len(args))
      _check_unique_non_empty(argnums)
      if isinstance(argnums, int):
          if as_tuple:
              return (args[argnums],)
          else:
              return args[argnums]
      return tuple(args[i] for i in argnums)
  
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 22 for calling a global function with example usage: _slice_argnums(args, argnums, as_tuple) and returns: tuple, args, args