Updating docstring of a class according to PEP 257 in a file (#4449)

* one class docs updated

* Updated with docs with PEP 257 in one class

* Updated with docs with PEP 257 in one class

* updated

* update

* Update

* update

* Update pymc3/step_methods/metropolis.py

Co-authored-by: Thomas Wiecki <[email protected]>

Author: ashish-hacker

pymc3/step_methods/metropolis.py

@@ -95,29 +95,7 @@ def __call__(self, num_draws=None):
 
 
 class Metropolis(ArrayStepShared):
-    """
-    Metropolis-Hastings sampling step
-
-    Parameters
-    ----------
-    vars: list
-        List of variables for sampler
-    S: standard deviation or covariance matrix
-        Some measure of variance to parameterize proposal distribution
-    proposal_dist: function
-        Function that returns zero-mean deviates when parameterized with
-        S (and n). Defaults to normal.
-    scaling: scalar or array
-        Initial scale factor for proposal. Defaults to 1.
-    tune: bool
-        Flag for tuning. Defaults to True.
-    tune_interval: int
-        The frequency of tuning. Defaults to 100 iterations.
-    model: PyMC Model
-        Optional model for sampling step. Defaults to None (taken from context).
-    mode:  string or `Mode` instance.
-        compilation mode passed to Theano functions
-    """
+    """Metropolis-Hastings sampling step"""
 
     name = "metropolis"
 
@@ -144,6 +122,28 @@ def __init__(
         mode=None,
         **kwargs
     ):
+        """Create an instance of a Metropolis stepper
+
+        Parameters
+        ----------
+        vars: list
+            List of variables for sampler
+        S: standard deviation or covariance matrix
+            Some measure of variance to parameterize proposal distribution
+        proposal_dist: function
+            Function that returns zero-mean deviates when parameterized with
+            S (and n). Defaults to normal.
+        scaling: scalar or array
+            Initial scale factor for proposal. Defaults to 1.
+        tune: bool
+            Flag for tuning. Defaults to True.
+        tune_interval: int
+            The frequency of tuning. Defaults to 100 iterations.
+        model: PyMC Model
+            Optional model for sampling step. Defaults to None (taken from context).
+        mode: string or `Mode` instance.
+            compilation mode passed to Theano functions
+        """
 
         model = pm.modelcontext(model)