code: def assert_non_empty_tensor_output(
      output: List[Any], api: str
  ) -> None:
      if output == [None] or len(output) < 1:
          raise RuntimeError(
              f"{api}: Expected f to be a function that has non-empty output (got output = {output})"
          )
      for o in output:
          if not isinstance(o, torch.Tensor):
              raise RuntimeError(
                  f"{api}: expected f(*primals) to return only tensors"
                  f", got unsupported type {type(o)}"
              )
  
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 25 for calling a global function with example usage: assert_non_empty_tensor_output(output, api) without return types