feat(fft): Implement scaled FFT primitive to optimize VJP

[Rudra-Tiwari-codes]

Proposed changes

This PR implements a scale parameter for the FFT primitive and updates the FFT::vjp (Vector Jacobian Product) to utilize this parameter. Previously, the VJP for FFT involved explicit Multiply nodes to handle 1/N scaling and symmetry masks. By integrating the scaling factor directly into the FFT primitive:
The computation graph is simplified (fewer nodes).
Performance is potentially improved by fusing the scaling operation into the FFT kernel execution on both CPU and Metal backends.
The FFT primitive constructor and is_equivalent checks have been updated to support the new member variable.

Checklist

Put an x in the boxes that apply.

• [x ] I have read the CONTRIBUTING document
• [ x] I have run pre-commit run --all-files to format my code / installed pre-commit prior to committing changes
• [x ] I have added tests that prove my fix is effective or that my feature works
• [ x] I have updated the necessary documentation (if needed)

[fransafu]

Warning: This author is forking multiple ML projects such as google-deepmind/alphafold, ml-explore/mlx, openai/CLIP, pytorch/pytorch, tensorflow/tensorflow, anthropics/claude-code, vllm-project/vllm, and others, adding minimal "contributions" (often for tests or miscellaneous changes) without proper validation. A review of their commits shows mostly local implementations of TODOs copied from existing projects, with little to no substantive review or testing.

So far, this author has forked 41 repositories following the same pattern. Be careful when accepting this PR. It’s also concerning how this author is able to submit PRs across four repositories in the same day, each requiring large context, which strongly suggests a highly automated workflow.