def tensor_to_arraybox(x, *args):
"""Convert a :class:`~.tensor` to an Autograd ``ArrayBox``.
Args:
x (array_like): Any data structure in any form that can be converted to
an array. This includes lists, lists of tuples, tuples, tuples of tuples,
tuples of lists and ndarrays.
Returns:
autograd.numpy.numpy_boxes.ArrayBox: Autograd ArrayBox instance of the array
Raises:
NonDifferentiableError: if the provided tensor is non-differentiable
"""
if isinstance(x, tensor):
if x.requires_grad:
return ArrayBox(x, *args)
raise NonDifferentiableError(
f"{x} is non-differentiable. Set the requires_grad attribute to True."
)
return ArrayBox(x, *args)
Box.type_mappings[tensor] = tensor_to_arraybox
VSpace.mappings[tensor] = lambda x: ComplexArrayVSpace(x) if onp.iscomplexobj(
x) else ArrayVSpace(x)
:class:`~.tensor` using Autograd."""
def tensor_to_arraybox(x, *args):
"""Convert a :class:`~.tensor` to an Autograd ``ArrayBox``.
Args:
x (array_like): Any data structure in any form that can be converted to
an array. This includes lists, lists of tuples, tuples, tuples of tuples,
tuples of lists and ndarrays.
Returns:
autograd.numpy.numpy_boxes.ArrayBox: Autograd ArrayBox instance of the array
Raises:
NonDifferentiableError: if the provided tensor is non-differentiable
"""
if isinstance(x, tensor):
if x.requires_grad:
return ArrayBox(x, *args)
raise NonDifferentiableError(
"{} is non-differentiable. Set the requires_grad attribute to True.".format(x)
)
return ArrayBox(x, *args)
Box.type_mappings[tensor] = tensor_to_arraybox
VSpace.mappings[tensor] = lambda x: ComplexArrayVSpace(x) if onp.iscomplexobj(x) else ArrayVSpace(x)