code: def safe_unpack_dual(dual, strict):
      if not isinstance(dual, torch.Tensor):
          raise RuntimeError(
              f"{jvp_str}: expected f(*args) to return only tensors"
              f", got unsupported type {type(dual)}"
          )
  
      primal, tangent = fwAD.unpack_dual(dual)
      if tangent is None:
          if strict:
              raise RuntimeError(
                  "jvp(f, primals, tangents, strict=True): "
                  "The output of f is independent of "
                  "the inputs. This is not allowed with strict=True."
              )
          tangent = torch.zeros_like(primal)
      return primal, tangent
  
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 28 for calling a global function with example usage: safe_unpack_dual(dual, strict) and returns: primal,  tangent