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vikashgericspod
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fix typos in docs: lazy_resampling, modules, whatsnew_1_5 (#9012)
Fixes # . ### Description Fixed 4 spelling typos in the documentation files: lazy_resampling.rst : "shwoing" → "showing", "midele" → "middle" modules.md : "procoess" → "process" whatsnew_1_5.md : "correspoinding" → "corresponding" No functional or code changes were made. ### Types of changes <!--- Put an `x` in all the boxes that apply, and remove the not applicable items --> - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [x] Documentation updated, tested `make html` command in the `docs/` folder. Signed-off-by: Vikash Gupta <write2vikash@gmail.com> Co-authored-by: Eric Kerfoot <17726042+ericspod@users.noreply.github.com>
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docs/source/lazy_resampling.rst

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@@ -253,7 +253,7 @@ so the user must set lazy=True on the transforms that they still wish to execute
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.. figure:: ../images/lazy_resampling_none_example.svg
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Figure shwoing the effect of using ``lazy=False`` when ``Compose`` is being executed with ``lazy=None``. Note that
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Figure showing the effect of using ``lazy=False`` when ``Compose`` is being executed with ``lazy=None``. Note that
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the additional resamples that occur due to ``RandRotate90d`` being executed in a non-lazy fashion.
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.. figure:: ../images/lazy_resampling_apply_pending_example.svg
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Figure showing the use of :class:`ApplyPendingd<monai.transforms.lazy.dictionary.ApplyPendingd>` to cause
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resampling to occur in the midele of a chain of lazy transforms.
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resampling to occur in the middle of a chain of lazy transforms.

docs/source/modules.md

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### EnsembleEvaluator
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A typical ensemble procoess is implemented as a ready-to-use workflow [[Cross validation and model ensemble tutorial]](https://github.com/Project-MONAI/tutorials/blob/main/modules/cross_validation_models_ensemble.ipynb):
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A typical ensemble process is implemented as a ready-to-use workflow [[Cross validation and model ensemble tutorial]](https://github.com/Project-MONAI/tutorials/blob/main/modules/cross_validation_models_ensemble.ipynb):
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1. Split all the training dataset into K folds.
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2. Train K models with every K-1 folds data.
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3. Execute inference on the test data with all the K models.

docs/source/whatsnew_1_5.md

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- Support numpy 2.x and Pytorch 2.6
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- MAISI inference accelerate
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- Bundles storage changed to huggingface and correspoinding api updated in core
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- Bundles storage changed to huggingface and corresponding api updated in core
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- Ported remaining generative tutorials and bundles
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- New tutorials:
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- [2d_regression/image_restoration.ipynb](https://github.com/Project-MONAI/tutorials/blob/main/2d_regression/image_restoration.ipynb)

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