fix typos in docs: lazy_resampling, modules, whatsnew_1_5

docs/source/lazy_resampling.rst

@@ -253,7 +253,7 @@ so the user must set lazy=True on the transforms that they still wish to execute
 
 .. figure:: ../images/lazy_resampling_none_example.svg
 
-    Figure shwoing the effect of using ``lazy=False`` when ``Compose`` is being executed with ``lazy=None``. Note that
+    Figure showing the effect of using ``lazy=False`` when ``Compose`` is being executed with ``lazy=None``. Note that
     the additional resamples that occur due to ``RandRotate90d`` being executed in a non-lazy fashion.
 
 
@@ -270,4 +270,4 @@ the following transform is a lazy transform, or is configured to execute lazily.
 .. figure:: ../images/lazy_resampling_apply_pending_example.svg
 
     Figure showing the use of :class:`ApplyPendingd<monai.transforms.lazy.dictionary.ApplyPendingd>` to cause
-    resampling to occur in the midele of a chain of lazy transforms.
+    resampling to occur in the middle of a chain of lazy transforms.

docs/source/modules.md

@@ -205,7 +205,7 @@ The workflow and some of MONAI event handlers are shown as below [[Workflow exam
 
 ### EnsembleEvaluator
 
-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):
+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):
 1. Split all the training dataset into K folds.
 2. Train K models with every K-1 folds data.
 3. Execute inference on the test data with all the K models.

docs/source/whatsnew_1_5.md

@@ -3,7 +3,7 @@
 
 - Support numpy 2.x and Pytorch 2.6
 - MAISI inference accelerate
-- Bundles storage changed to huggingface and correspoinding api updated in core
+- Bundles storage changed to huggingface and corresponding api updated in core
 - Ported remaining generative tutorials and bundles
 - New tutorials:
   - [2d_regression/image_restoration.ipynb](https://github.com/Project-MONAI/tutorials/blob/main/2d_regression/image_restoration.ipynb)