[Feature Request] Add new MAX ops `bitwise_and`, `bitwise_or` and `bitwise_xor`

What is your request?

torch functions:

• https://docs.pytorch.org/docs/stable/generated/torch.bitwise_and.html
• https://docs.pytorch.org/docs/stable/generated/torch.bitwise_or.html
• https://docs.pytorch.org/docs/stable/generated/torch.bitwise_xor.html

Note that we could provide ops with those names but also if we want to follow torch's api, we'll want to implement it in TensorValue's __and__, __or__ and __xor__. The behavior in pytorch for those is to use the bitwise version:

In [3]: torch.tensor([1, 2], dtype=torch.uint8) |  torch.tensor([2, 4], dtype=torch.uint8)
Out[3]: tensor([3, 6], dtype=torch.uint8)

What is your motivation for this change?

I need it to implement the max backend for torch, and surprisingly it's used in gemma3 (or at least it's what torch aotautograd decomposes some ops into).

Any other details?

I could code them in torch-max-backend directly in Mojo, but then I end up with this bug: https://github.com/modular/modular/issues/5299

Start with the linked PyTorch bitwise_and, bitwise_or, and bitwise_xor documentation and inspect the MAX TensorValue operator behavior. Reproduce the uint8 example from the issue, then verify that the named ops and TensorValue's and, or, and xor provide the expected bitwise behavior for the torch MAX backend without the workaround described in issue 5299.