code: def error_if_complex(func_name, args, is_input):
      flat_args = pytree.tree_leaves(args)
      for idx, arg in enumerate(flat_args):
          if isinstance(arg, torch.Tensor) and arg.dtype.is_complex:
              input_or_output = "inputs" if is_input else "outputs"
              err_msg = (
                  f"{func_name}: Expected all {input_or_output} "
                  f"to be real but received complex tensor at flattened input idx: {idx}"
              )
              raise RuntimeError(err_msg)
  
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 14 for calling a global function with example usage: error_if_complex(func_name, args, is_input) without return types