Add Toto 2.0 model wrapper#155
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- Reuse Datadog's GluonTS integration (ffill imputation, scaler fallback, patch-size rounding) via fev's gluonts adapter, mirroring the moirai wrapper. - trained_on_datasets = [] (Toto 2.0 has no overlap with fev datasets). - Drop the seed arg (model is deterministic).
- Split univariate/multivariate post-processing into _format_predictions (no more flatten-then-recombine round-trip for the multivariate path). - batch_size 128 -> 512 to match the GIFT-Eval benchmark notebook (context_length=4096 and model_path=Toto-2.0-22m already matched).
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Adds a wrapper for Toto 2.0 (Datadog) under
models/toto-2.0/.toto-2PyPI package and Toto 2.0'sToto2GluonTSModelAPI.