Improve type handling in PyTorch frontend (#5834)

Improve type handling in PyTorch frontend (#5834)

* Improve type handling in PyTorch frontend

- Use type information from graph for inputs if available. Check
  against shape information from graph if available.
- Allow user to set default dtype (default to float32 for sanity and
  compatibility).
- Implement type promotion to follow PyTorch mechanism. This includes
  fixing the handling of many "Scalar" overloads in PyTorch binary ops.
- Fix arange/linspace type semantics.
- Added support for traced functions. (Because it really is about the
  "self" input handling.)

Aside from adding an optional default_dtype keyword argument, this does not
change the signature/requirements of from_pytorch.

* Fix scalar detection using numpy.isscalar

and address other review comments. Thank you @siju-samuel

* refine test criteron on qnn_test::test_serialized_modules, fix bool conversion of const