fix: handle device in the same way as dtype in aten.full_like decomposition

[junstar92]

Description

This PR extends the changes introduced in PR #3535 by applying similar handling for the device, which was previously missed.

In the original PR, the focus was on ensuring the correct propagation of dtype when using torch.full_like. However, torch.full_like also accepts a device argument, and if a device is explicitly passed, it may differ from the input tensor's device. This can result in the output tensor being created on a different device than the input, leading to device mismatch issues.

import torch
from torch.export._trace import _export
from torch_tensorrt.dynamo.lowering import get_decompositions

device0 = torch.device("cuda", index=0)
device1 = torch.device("cuda", index=1)


class MyModel(torch.nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x):
        return torch.ones_like(x, dtype=torch.bool, device=torch.device("cuda", index=1))


model = MyModel().to(device0)
x = torch.randn(1, 10, dtype=torch.float16).to(device0)
ep = _export(model, (x,))
ep = ep.run_decompositions(get_decompositions(False))
gm = ep.module()
y = gm(x)

assert y.device == device1, f"{device1} expected, but got {y.device}"

Results:

AssertionError: cuda:1 expected, but got cuda:0

To prevent this, this PR ensures that the device is handled in the same way as dtype in the previous PR.

Type of change

• Bug fix (non-breaking change which fixes an issue)

Checklist:

• My code follows the style guidelines of this project (You can use the linters)
• I have performed a self-review of my own code
• I have commented my code, particularly in hard-to-understand areas and hacks
• I have made corresponding changes to the documentation
• I have added tests to verify my fix or my feature
• New and existing unit tests pass locally with my changes
• I have added the relevant labels to my PR in so that relevant reviewers are notified

[peri044]

LGTM