Update docstring formatting

Author: awray3

RELEASE-NOTES.md

@@ -20,6 +20,7 @@ It also brings some dreadfully awaited fixes, so be sure to go through the chang
 - `OrderedProbit` distribution added (see [#4232](https://github.com/pymc-devs/pymc3/pull/4232)).
 - `plot_posterior_predictive_glm` now works with `arviz.InferenceData` as well (see [#4234](https://github.com/pymc-devs/pymc3/pull/4234))
 - Add `logcdf` method to all univariate discrete distributions (see [#4387](https://github.com/pymc-devs/pymc3/pull/4387)).
+- Add `random` method to `MvGaussianRandomWalk` (see [#4388](https://github.com/pymc-devs/pymc3/issues/4337))
 
 ### Maintenance
 - Fixed bug whereby partial traces returns after keyboard interrupt during parallel sampling had fewer draws than would've been available [#4318](https://github.com/pymc-devs/pymc3/pull/4318)

pymc3/distributions/timeseries.py

@@ -485,24 +485,22 @@ def random(self, point=None, size=None):
 
         Examples
         -------
-        Create one sample from a 2-dimensional Gaussian random walk with 10 timesteps::
+        .. code-block:: python
 
-            mu = np.array([1.0, 0.0])
-            cov = np.array([[1.0, 0.0], [0.0, 2.0]])
-            sample = MvGaussianRandomWalk(mu, cov, shape=(10, 2)).random()
+            with pm.Model():
+                mu = np.array([1.0, 0.0])
+                cov = np.array([[1.0, 0.0],
+                                [0.0, 2.0]])
 
-        Create three samples from a 2-dimensional Gaussian random walk with 10 timesteps::
+                # draw one sample from a 2-dimensional Gaussian random walk with 10 timesteps
+                sample = MvGaussianRandomWalk(mu, cov, shape=(10, 2)).random()
 
-            mu = np.array([1.0, 0.0])
-            cov = np.array([[1.0, 0.0], [0.0, 2.0]])
-            sample = MvGaussianRandomWalk(mu, cov, shape=(10, 2)).random(size=3)
+                # draw three samples from a 2-dimensional Gaussian random walk with 10 timesteps
+                sample = MvGaussianRandomWalk(mu, cov, shape=(10, 2)).random(size=3)
 
-        Create four samples from a 2-dimensional Gaussian random walk with 10
-        timesteps, indexed with a (2, 2) array::
-
-            mu = np.array([1.0, 0.0])
-            cov = np.array([[1.0, 0.0], [0.0, 2.0]])
-            sample = MvGaussianRandomWalk(mu, cov, shape=(10, 2)).random(size=(2, 2))
+                # draw four samples from a 2-dimensional Gaussian random walk with 10 timesteps,
+                # indexed with a (2, 2) array
+                sample = MvGaussianRandomWalk(mu, cov, shape=(10, 2)).random(size=(2, 2))
         """
 
         # for each draw specified by the size input, we need to draw time_steps many