Restyle add example with python parameters file

content/docs/command-reference/params/index.md

@@ -24,8 +24,8 @@ dependencies: _parameters_. Parameters are defined using the the `-p`
 
 In contrast to a regular <abbr>dependency</abbr>, a parameter is not a file (or
 directory). Instead, it consists of a _parameter name_ (or key) to find inside a
-YAML, JSON, or TOML _parameters file_. Multiple parameter dependencies can be
-specified from one or more parameters files.
+YAML, JSON, TOML, or Python _parameters file_. Multiple parameter dependencies
+can be specified from one or more parameters files.
 
 The default parameters file name is `params.yaml`. Parameters should be
 organized as a tree hierarchy inside, as DVC will locate param names by their
@@ -91,8 +91,8 @@ $ dvc run -n train -d users.csv -o model.pkl \
           python train.py
 ```
 
-> Note that we could use the same parameter addressing with JSON or TOML
-> parameters files.
+> Note that we could use the same parameter addressing with JSON, TOML, or
+> Python parameters files.
 
 The `train.py` script will have some code to parse the needed parameters. For
 example:
@@ -143,6 +143,77 @@ $ dvc run -n train -d logs/ -o users.csv \
           python train.py
 ```
 
+## Examples: Python parameters file
+
+Consider this parameters file in Python format, named `params.py`:
+
+```python
+IS_BOOL: bool = True
+CONST = 5
+
+
+class TrainConfig:
+    EPOCHS = 70
+
+    def __init__(self):
+        self.layers = 9
+
+
+class TestConfig:
+    TEST_DIR = "path"
+    METRICS = ["metric"]
+```
+
+The following [stage](/doc/command-reference/run) depends on params `IS_BOOL`,
+`CONST`, as well as `TrainConfig`'s `EPOCHS` and `layers`:
+
+```dvc
+$ dvc run -n train -d users.csv -o model.pkl \
+          -p params.py:IS_BOOL,CONST,TrainConfig.EPOCHS,TrainConfig.layers \
+          python train.py
+```
+
+Resulting `dvc.yaml` and `dvc.lock` files (notice the `params` list):
+
+```yaml
+stages:
+  train:
+    cmd: python train.py
+    deps:
+      - users.csv
+    params:
+      - IS_BOOL
+      - CONST
+      - TrainConfig.EPOCHS
+      - TrainConfig.layers
+    outs:
+      - model.pkl
+```
+
+```yaml
+train:
+  cmd: python train.py
+  deps:
+    - path: users.csv
+      md5: 23be4307b23dcd740763d5fc67993f11
+  params:
+    CONST: 5
+    IS_BOOL: true
+    TrainConfig.EPOCHS: 70
+    TrainConfig.layers: 9
+  outs:
+    - path: model.pkl
+      md5: 1c06b4756f08203cc496e4061b1e7d67
+```
+
+Alternatively, the entire `TestConfig` group can be referenced, instead of the
+parameters in it:
+
+````dvc
+$ dvc run -n train -d users.csv -o model.pkl \
+          -p params.py:IS_BOOL,CONST,TestConfig \
+          python train.py
+
 ## Examples: Print all parameters
 
 Following the previous example, we can use `dvc params diff` to list all of the
@@ -161,3 +232,4 @@ params.yaml  train.layers    None   9
 This command shows the difference in parameters between the workspace and the
 last committed version of the `params.yaml` file. In our example there's no
 previous version, which is why all `Old` values are `None`.
+````

content/docs/command-reference/run.md

@@ -114,8 +114,8 @@ Relevant notes:
 
 [parameters](/doc/command-reference/params) (`-p`/`--params` option) are a
 special type of key/value dependencies. Multiple parameter dependencies can be
-specified from within one or more YAML, JSON or TOML parameters files (e.g.
-`params.yaml`). This allows tracking experimental hyperparameters easily.
+specified from within one or more YAML, JSON, TOML, or Python parameters files
+(e.g. `params.yaml`). This allows tracking experimental hyperparameters easily.
 
 Special types of output files, [metrics](/doc/command-reference/metrics) (`-m`
 and `-M` options) and [plots](/doc/command-reference/plots) (`--plots` and

content/docs/start/experiments.md

@@ -100,7 +100,7 @@ parameters.
 It's pretty common for data science pipelines to include configuration files
 that define adjustable parameters to train a model, do pre-processing, etc. DVC
 provides a mechanism for stages to depend on the values of specific sections of
-such a config file (YAML, JSON and TOML formats are supported).
+such a config file (YAML, JSON, TOML, and Python formats are supported).
 
 Luckily, we should already have a stage with
 [parameters](/doc/command-reference/params) in `dvc.yaml`:

content/docs/user-guide/basic-concepts/parameter.md

@@ -4,5 +4,5 @@ match: [parameter, parameters, param, params, hyperparameter, hyperparameters]
 ---
 
 Pipeline stages (defined in `dvc.yaml`) can depend on specific values inside an
-arbitrary YAML, JSON, or TOML file (`params.yaml` by default). Stages are
-invalidated when any of their parameter values change. See `dvc param`.
+arbitrary YAML, JSON, TOML, or Python file (`params.yaml` by default). Stages
+are invalidated when any of their parameter values change. See `dvc param`.

content/docs/user-guide/dvc-files-and-directories.md

@@ -150,7 +150,7 @@ the possible following fields:
 - `deps`: List of <abbr>dependency</abbr> file or directory paths of this stage
   (relative to `wdir` which defaults to the file's location)
 - `params`: List of <abbr>parameter</abbr> dependency keys (field names) that
-  are read from a YAML, JSON, or TOML file (`params.yaml` by default).
+  are read from a YAML, JSON, TOML, or Python file (`params.yaml` by default).
 - `outs`: List of <abbr>output</abbr> file or directory paths of this stage
   (relative to `wdir` which defaults to the file's location), and optionally,
   whether or not this file or directory is <abbr>cached</abbr> (`true` by