Updat .pre-commit hook version

.pre-commit-config.yaml

@@ -3,13 +3,19 @@ repos:
     hooks:
       - id: ruff
         name: ruff
-        entry: ruff
-        args: ["--fix", "--show-source"]
-        files: "numpyro/.*|tests/.*"
+        entry: ruff check
+        args: ["--fix", "--output-format=full"]
+        files: "(.py$)|(.*.ipynb$)"
         language: system
 
+      - id: ruff-format
+        name: ruff-format
+        entry: ruff format
+        language: system
+        files: "(.py$)|(.*.ipynb$)"
+
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.5.0
+    rev: v4.6.0
     hooks:
       - id: debug-statements
       - id: check-yaml

examples/cvae-flax/README.md

@@ -1,12 +1,11 @@
 ## Conditional Variational Autoencoder in Flax
 
-Trains a *Conditional Variational Autoencoder* (CVAE) on the MNIST data using Flax' neural network API. 
+Trains a *Conditional Variational Autoencoder* (CVAE) on the MNIST data using Flax' neural network API.
 
 The model first trains a baseline to predict an entire MNIST image from a single quadrant of it (i.e., input is one quadrant of an image, output is the entire image (not the other three quadrants)).
 Then, in a second model, the generation/prior/recognition nets of the CVAE are trained while keeping the model parameters of the baseline fixed/frozen.
 We use Optax' `multi_transform` to apply different gradient transformations to the trainable parameters and the frozen parameters.
 
 Running `main.py` trains the model(s) and plots a figure in the end comparing the baseline prediction with the CVAE prediction like this one:
 
-![CVAE prediction](https://github.com/pyro-ppl/numpyro/tree/master/docs/source/_static/img/examples/cvae.png) 
-
+![CVAE prediction](https://github.com/pyro-ppl/numpyro/tree/master/docs/source/_static/img/examples/cvae.png)