Add support to more feasiable function to.

[hzhangxyz]

No description provided.

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 38.57%. Comparing base (f6fb29e) to head (9508f04).
⚠️ Report is 4 commits behind head on main.

Additional details and impacted files

@@            Coverage Diff             @@
##             main      #23      +/-   ##
==========================================
+ Coverage   28.81%   38.57%   +9.76%     
==========================================
  Files           3        3              
  Lines         177      197      +20     
  Branches       19       26       +7     
==========================================
+ Hits           51       76      +25     
+ Misses        126      121       -5     

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[Copilot]

Pull Request Overview

This PR enhances the to method of the ParityTensor class to support more flexible conversion options, including device and dtype conversions with multiple parameter formats. The method now accepts positional arguments for device/dtype specifications alongside keyword arguments.

• Expands the to method signature to accept torch.device, torch.dtype, or string parameters
• Adds comprehensive test coverage for various parameter combinations and error cases
• Implements validation to prevent duplicate parameter specifications

Reviewed Changes

Copilot reviewed 2 out of 2 changed files in this pull request and generated 1 comment.

File	Description
parity_tensor/parity_tensor.py	Enhanced to method with flexible parameter handling and dtype conversion support
tests/conversion_test.py	Added comprehensive test suite covering parameter combinations and edge cases

Comments suppressed due to low confidence (2)

tests/conversion_test.py:41

• The condition len(args) <= 1 means cases with 2 positional arguments (device + dtype) are never tested, leaving important functionality uncovered.

    if len(args) <= 1:

parity_tensor/parity_tensor.py:67

• The parameter name whatever is unclear and unprofessional. Consider renaming it to something more descriptive like target or destination.

    def to(self, whatever: torch.device | torch.dtype | str | None = None, *, device: torch.device | None = None, dtype: torch.dtype | None = None) -> ParityTensor: