torch.func.jacfwd
torch.func.jacfwd(func, argnums=0, has_aux=False, *, randomness='error')
Computes the Jacobian of "func" with respect to the arg(s) at index
   "argnum" using forward-mode autodiff
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
    auxiliary objects that will not be differentiated. Default:
    False.

  * **randomness** (*str*) -- Flag indicating what type of
    randomness to use. See "vmap()" for more detail. Allowed:
