text: import torch
  from .grad_mode import _DecoratorContextManager
  from collections import namedtuple
  
  from typing import Any
  
  __all__ = ["UnpackedDualTensor", "enter_dual_level", "exit_dual_level", "make_dual", "unpack_dual", "dual_level"]
  
  # Global variable used to make the python API simpler to use
  _current_level = -1
  
  def enter_dual_level():
      r"""Function that can be used to enter a new forward grad level.
      This level can be used to make and unpack dual Tensors to compute
      forward gradients.
  
      This function also updates the current level that is used by default
      by the other functions in this API.
      """
      global _current_level
      new_level = torch._C._enter_dual_level()
      if new_level != _current_level + 1:
          raise RuntimeError("Entering a new forward AD level but the current level "
                             "is not valid. Make sure you did not modified it directly.")
      _current_level = new_level
      return new_level
  
  def exit_dual_level(*, level=None):
      r"""Function that can be used to exit a forward grad level.
      This function deletes all the gradients associated with this
      level. Only deleting the latest entered level is allowed.
  
      This function also updates the current level that is used by default
      by the other functions in this API.
      """
      global _current_level
      if level is None:
          level = _current_level
      if level != _current_level:
          raise RuntimeError("Trying to exit a forward AD level that was not the last one "
                             "that was created. This is not supported.")
      torch._C._exit_dual_level(level=level)
      _current_level = level - 1
  
  def make_dual(tensor, tangent, *, level=None):
      r"""Associates a tensor value with a forward gradient, the tangent, to create a
      "dual tensor", which is used to compute forward AD gradients.
      The result is a new tensor aliased to :attr:`tensor` with :attr:`tangent` embedded
      as an attribute as-is if it has the same storage layout or copied otherwise.
      The tangent attribute can be recovered with :func:`unpack_dual`.
  
      This function is backward differentiable.
  
      Given a function `f` whose jacobian is `J`, it allows one to compute the Jacobian-vector product (`jvp`)
      between `J` and a given vector `v` as follows.
  
      Example::
  
          >>> with dual_level():
          ...   inp = make_dual(x, v)
          ...   out = f(inp)
          ...   y, jvp = unpack_dual(out)
  
      Please see the `forward-mode AD tutorial <https://pytorch.org/tutorials/intermediate/forward_ad_usage.html>`__
      for detailed steps on how to use this API.
  
      """
      if level is None:
          level = _current_level
  
      if level < 0:
          raise RuntimeError("Trying to create a dual Tensor for forward AD but no level "
                             "exists, make sure to enter_dual_level() first.")
      if not (tensor.is_floating_point() or tensor.is_complex()):
          raise ValueError(f"Expected primal to be floating point or complex, but got: {tensor.dtype}")
      if not (tangent.is_floating_point() or tangent.is_complex()):
          raise ValueError(f"Expected tangent to be floating point or complex, but got: {tangent.dtype}")
  
      return torch._VF._make_dual(tensor, tangent, level=level)
  
  _UnpackedDualTensor = namedtuple('_UnpackedDualTensor', ['primal', 'tangent'])
  
  class UnpackedDualTensor(_UnpackedDualTensor):
      r"""Namedtuple returned by :func:`unpack_dual` containing the primal and tangent components of the dual tensor.
      See :func:`unpack_dual` for more details."""
      pass
  
  def unpack_dual(tensor, *, level=None):
      r"""Unpacks a "dual tensor" to get both its Tensor value and its forward AD gradient.
      The result is a namedtuple ``(primal, tangent)`` where ``primal`` is a view of
      :attr:`tensor`'s primal and ``tangent`` is :attr:`tensor`'s tangent as-is.
      Neither of these tensors can be dual tensor of level :attr:`level`.
  
      This function is backward differentiable.
  
      Example::
  
          >>> with dual_level():
          ...   inp = make_dual(x, x_t)
          ...   out = f(inp)
          ...   y, jvp = unpack_dual(out)
          ...   jvp = unpack_dual(out).tangent
  
      Please see the `forward-mode AD tutorial <https://pytorch.org/tutorials/intermediate/forward_ad_usage.html>`__
      for detailed steps on how to use this API.
      """
      if level is None:
          level = _current_level
  
      if level < 0:
          return UnpackedDualTensor(tensor, None)
  
      primal, dual = torch._VF._unpack_dual(tensor, level=level)
  
      return UnpackedDualTensor(primal, dual)
  
  class dual_level(_DecoratorContextManager):
      r"""Context-manager that enables forward AD. All forward AD computation must
      be performed in a ``dual_level`` context.
  
      .. Note::
  
          The ``dual_level`` context appropriately enters and exit the dual level to
          controls the current forward AD level, which is used by default by the other
          functions in this API.
  
          We currently don't plan to support nested ``dual_level`` contexts, however, so
          only a single forward AD level is supported. To compute higher-order
          forward grads, one can use `functorch's jvp <https://github.com/pytorch/functorch#jvp>`__.
  
      Example::
  
          >>> x = torch.tensor([1])
          >>> x_t = torch.tensor([1])
          >>> with dual_level():
          ...   inp = make_dual(x, x_t)
          ...   # Do computations with inp
          ...   out = your_fn(inp)
          ...   _, grad = unpack_dual(out)
          >>> grad is None
          False
          >>> # After exiting the level, the grad is deleted
          >>> _, grad_after = unpack_dual(out)
          >>> grad is None
          True
  
      Please see the `forward-mode AD tutorial <https://pytorch.org/tutorials/intermediate/forward_ad_usage.html>`__
      for detailed steps on how to use this API.
      """
      def __init__(self):
          super().__init__()
  
      def __enter__(self):
          return enter_dual_level()
  
      def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
          exit_dual_level()
  