feat: add functions to compute logit statistics

[dtch1997]

In order to fairly compare logit-difference-based statistics across different datasets, it is important to record general statistics about the distribution of logits under steering vectors. This PR adds functionality to compute some moment- and quantile-based statistics

Summary by CodeRabbit

• New Features
  • Enhanced the TokenProb class in the Pipeline with additional attributes and properties for statistical metrics.
  • Introduced new functions to compute statistical moments and quantiles of tensors within the Pipeline class.
  • Utilized torch, scipy, and numpy for testing purposes in the test_pipeline.py file.

[coderabbitai]

Walkthrough

The changes encompass enhancing the TokenProb class in pipeline.py with additional attributes for statistical metrics and introducing functions to compute moments and quantiles of tensors. The Pipeline class now incorporates these calculations for logits in the generation pipeline.

Changes

File	Summary
repepo/core/pipeline.py	TokenProb class updated with additional attributes for statistical metrics and properties for logit_max and logit_min. New functions compute_moments and compute_quantiles added. Pipeline class now calculates moments and quantiles for logits.
tests/core/test_pipeline.py	Added imports for torch, scipy, and numpy. New functions added for computing moments and quantiles. Modified Pipeline import in repepo.core.pipeline to include compute_moments and compute_quantiles.

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