)

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------------------------------------------------------------------------------------------------------------

**Context:** When Torch has a GPU backed data-buffer, failures can occur
when attempting to make autoray-dispatched calls to Torch method with
paired CPU data. In this case, for probabilities on the GPU, and
eigenvalues on the host (read from the observables), failures appeared
with `qml.dot`, and can be reproduced from:

```python
import pennylane as qml
import torch
import numpy as np

torch_device="cuda"
dev = qml.device("default.qubit.torch", wires=2, torch_device=torch_device)
ham = qml.Hamiltonian(torch.tensor([0.1, 0.2], requires_grad=True), [qml.PauliX(0), qml.PauliZ(1)])

@qml.qnode(dev, diff_method="backprop", interface="torch")
def circuit():
    qml.RX(np.zeros(5), 0)  # Broadcast the state by applying a broadcasted identity
    return qml.expval(ham)

res = circuit()
assert qml.math.allclose(res, 0.2)
```

This pair modifies the registered `coerce` method for Torch to always
automigrate mixed CPU-GPU data to always favour the associated GPU. In
addition, this method now also catches multi-GPU data, where tensors do
not reside on the same index, and will fail outright. As a longer term
solution, moving the Torch GPU dispatch calls to earlier in the stack
would be more sound, but this fixes the aforementioned issue, at the
expense of always migrating from CPU to GPU.

**Description of the Change:** As above.

**Benefits:** Allows automatic data migration from host to device when
using a GPU backed tensor. In addition, will catch multi-GPU tensor data
when using Torch, and fail due to non-local representations.

**Possible Drawbacks:** Auto migration may not always be wanted. The
alternative solution is to always be explicit about locality, and move
the eigenvalue data to exist on the device at a higher layer in the
stack.

**Related GitHub Issues:** #5269 introduced changes that resulted in GPU
errors.