Arm backend: Support division with integer tensors

[Erik-Lundell]

Arm backend: Support integer division.

torch.div() with two integer tensors and a rounding mode
gives integer output. The decomposition for this case instead
yielded floating point output, causing issues in indexing
operations which is typically where such integer division happens.

When supported, utilize the TOSA operator INTDIV. It directly
corresponds to the trunc case, and can be adjusted in the floor case.
When not supported, use float path by first casting int tensors
to float, and then casting the output back.

Additionally
- Improve scalar handling.
- Add cast op to u55 testrunner to match u85 better.

Arm backend: Support quantizing no-op fp casts.

If a int to fp cast goes directly into a quantized
operator, the cast is a noop if it is quantized
with unit quantization parameters (scale=1, zp=0).
This means it can be handled, even if the backend
doesn't support fp.

- Add Fixed unit qparam annotation to such a cast.
- Allow it in the partitioner.
- Finally, make sure the cast dtype is correct after
folding quant nodes.

This pattern can appear in the wild in a model,
or in a decomposition where you want to quantize
an integer tensor.

cc @digantdesai @freddan80 @per @zingo @oscarandersson8218 @mansnils @Sebastian-Larsson @robell @rascani

[pytorch-bot]

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