`Tensor.mean` missing backward implementation
Nobody has claimed this yet.
Assessment
- Difficulty
- 2/5
- Estimated time
- 1-3 hours
- Newbie friendliness
- 75/100
- Issue type
- Bug
- Clarity
- Clearly specified
- Activity status
- Active
- Tech stack
- python
- Domain
- machine-learning
Research direction
Start in leanpass/tensor.py and read the mean method, especially its unfinished if self.requires_grad: block. Check how nearby tensor operations define backward closures, then verify that gradients flow through mean in the loss calculations it supports.
Written by the indexing model from the issue text.
Description
File: leanpass/tensor.py
The mean method in leanpass/tensor.py ends with an unfinished block (if self.requires_grad:) and never defines a backward closure. Consequently, gradients never flow through a mean operation, breaking many loss calculations that rely on it.
Filed automatically by ai-issue-scan.
- Dominant language
- Python
- Stars
- 16
- Forks
- 1
- Avg merge
- 2d 8h
- Merged PRs (30d)
- 5
Getting set up
- No Dockerfile or Docker Compose file
- Has a pull request template
- Read the contributing guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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