def _wrapped_unary_to_nary(func):
"""Use functools.wraps with unary_to_nary decorator."""
if AUTOGRAD_AVAILABLE:
return wraps(func)(unary_to_nary(func))
else:
return func
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This module contains the autograd wrappers :class:`grad` and :func:`jacobian`
"""
import numpy as onp
from pennylane import numpy as np
from functools import partial
from autograd.core import make_vjp as _make_vjp
from autograd.wrap_util import unary_to_nary
from autograd.extend import vspace
from autograd import jacobian as _jacobian
make_vjp = unary_to_nary(_make_vjp)
class grad:
"""Returns the gradient as a callable function of (functions of) QNodes.
Function arguments with the property ``requires_grad`` set to ``False``
will automatically be excluded from the gradient computation, unless
the ``argnum`` keyword argument is passed.
When the output gradient function is executed, both the forward pass
*and* the backward pass will be performed in order to
compute the gradient. The value of the forward pass is available via the
:attr:`~.forward` property.
Args:
def _wrapped_unary_to_nary(func):
"""Use functools.wraps with unary_to_nary decorator."""
return wraps(func)(unary_to_nary(func))
