torch.func.jacrev
torch.func.jacrev(func, argnums=0, *, has_aux=False, chunk_size=None, _preallocate_and_copy=False)
Computes the Jacobian of "func" with respect to the arg(s) at index
   "argnum" using reverse mode autodiff
Note:
 Using "chunk_size=1" is equivalent to computing the jacobian row-
 by-row with a for-loop i.e. the constraints of "vmap()" are not
 applicable.

Parameters:
      * func (function) -- A Python function that takes one or
        more arguments, one of which must be a Tensor, and returns one
        or more Tensors
  * **argnums** (*int** or **Tuple**[**int**]*) -- Optional,
    integer or tuple of integers, saying which arguments to get
    the Jacobian with respect to. Default: 0.

  * **has_aux** (*bool*) -- Flag indicating that "func" returns a
    "(output, aux)" tuple where the first element is the output of
    the function to be differentiated and the second element is
