Add requirements and basic usage to normalizing flow example

flow/README.md

@@ -1,23 +1,46 @@
 # Normalizing flow
 
-Real NVP normalizing flow from [Dinh et al. (2016)](https://arxiv.org/abs/1605.08803) implemented using `mlx`. 
+Real NVP normalizing flow for density estimation and sampling from [Dinh et al. (2016)](https://arxiv.org/abs/1605.08803), implemented using `mlx`. 
 
-The example is written in a somewhat more object-oriented style than strictly necessary, with an eye towards extension to other use cases benefitting from arbitrary distributions and bijectors.
+The example is written in a somewhat more object-oriented style than strictly necessary, with an eye towards extension to other use cases that could from arbitrary distributions and bijectors.
 
-## Usage
+## Basic usage
 
-The example can be run with
+```py
+import mlx.core as mx
+from flows import RealNVP
+
+model = RealNVP(n_transforms=8, d_params=4, d_hidden=256, n_layers=4)
+
+x = mx.random.normal(shape=(32, 4))
+
+# Evaluate log-density
+model.log_prob(x=x)
+
+# Draw samples
+model.sample(sample_shape=(32, 4))
+```
+
+## Running the example
+
+Install the dependencies:
+
+```
+pip install -r requirements.txt
+```
+
+The example can be run with:
 ```
 python main.py
 ```
 which trains the normalizing flow on the two moons dataset and plots the result in `samples.png`. 
 
-By default the example runs on the GPU. To run on the CPU, do 
+By default the example runs on the GPU. To run on the CPU, do:
 ```
 python main.py --cpu
 ```
 
-For all available options, run
+For all available options, run:
 ```
 python main.py --help
 ```

flow/requirements.txt

@@ -0,0 +1,4 @@
+mlx
+numpy
+tqdm
+scikit-learn