DLPack deprecation working

[mrava87]

Fix calls to DLPack in TorchOperator based on this warning

/site-packages/pylops/_torchoperator.py:37: VisibleDeprecationWarning: This function is deprecated and will be removed in a future release. Use the cupy.from_dlpack() array constructor instead.
  x = cp.fromDlpack(to_dlpack(x))
/site-packages/pylops/_torchoperator.py:48: VisibleDeprecationWarning: This function is deprecated and will be removed in a future release. Use the cupy.from_dlpack() array constructor instead.
  y = from_dlpack(y.toDlpack())

[mrava87]

The fix is simple:

y = from_dlpack(y.toDlpack())   -> y = from_dlpack(y) 
x = from_dlpack(x.toDlpack())   -> x = from_dlpack(x)   

Final version:

class _TorchOperator(torch.autograd.Function):
    """Wrapper class for PyLops operators into Torch functions"""

    @staticmethod
    def forward(ctx, x, forw, adj, device, devicetorch):
        ctx.forw = forw
        ctx.adj = adj
        ctx.device = device
        ctx.devicetorch = devicetorch

        # check if data is moved to cpu and warn user
        if ctx.device == "cpu" and ctx.devicetorch != "cpu":
            warnings.warn(
                "PyLops operator will be applied on the cpu "
                "whilst the input torch vector is on "
                "%s, this may lead to poor performance" % ctx.devicetorch,
                stacklevel=2,
            )

        # prepare input
        if ctx.device == "cpu":
            # bring x to cpu and numpy
            x = x.cpu().detach().numpy()
        else:
            # pass x to cupy using DLPack
            x = cp.from_dlpack(to_dlpack(x))

        # apply forward operator
        y = ctx.forw(x)

        # prepare output
        if ctx.device == "cpu":
            # move y to torch and device
            y = torch.from_numpy(y).to(ctx.devicetorch)
        else:
            # move y to torch via the __dlpack__ protocol: pass the cupy array
            # directly instead of the deprecated y.toDlpack() capsule, which
            # torch.from_dlpack rejects under modern DLPack versioning
            y = from_dlpack(y)
        return y

    @staticmethod
    def backward(ctx, y):
        # prepare input
        if ctx.device == "cpu":
            y = y.cpu().detach().numpy()
        else:
            # pass x to cupy using DLPack
            y = cp.from_dlpack(to_dlpack(y))

        # apply adjoint operator
        x = ctx.adj(y)

        # prepare output
        if ctx.device == "cpu":
            x = torch.from_numpy(x).to(ctx.devicetorch)
        else:
            # move x to torch via the __dlpack__ protocol (see forward)
            x = from_dlpack(x)
        return x, None, None, None, None, None