Note that when "strict" is "False", the result can not require
        gradients or be disconnected from the inputs.  Defaults to
        "False".
  * **strict** (*bool**, **optional*) -- If "True", an error will
    be raised when we detect that there exists an input such that
    all the outputs are independent of it. If "False", we return a
    Tensor of zeros as the jvp for said inputs, which is the
    expected mathematical value. Defaults to "False".

Returns:
      tuple with:
         func_output (tuple of Tensors or Tensor): output of
         "func(inputs)"
     jvp (tuple of Tensors or Tensor): result of the dot product
     with the same shape as the output.

Return type:
      output (tuple)
Note:
 "autograd.functional.jvp" computes the jvp by using the backward
 of the backward (sometimes called the double backwards trick).
 This is not the most performant way of computing the jvp. Please
