[SPARK-3773][PySpark][Doc] Sphinx build warning

When building Sphinx documents for PySpark, we have 12 warnings.
Their causes are almost docstrings in broken ReST format.

To reproduce this issue, we should run following commands on the commit: 6e27cb6.

```bash
$ cd ./python/docs
$ make clean html
...
/Users/<user>/MyRepos/Scala/spark/python/pyspark/__init__.py:docstring of pyspark.SparkContext.sequenceFile:4: ERROR: Unexpected indentation.
/Users/<user>/MyRepos/Scala/spark/python/pyspark/__init__.py:docstring of pyspark.RDD.saveAsSequenceFile:4: ERROR: Unexpected indentation.
/Users/<user>/MyRepos/Scala/spark/python/pyspark/mllib/classification.py:docstring of pyspark.mllib.classification.LogisticRegressionWithSGD.train:14: ERROR: Unexpected indentation.
/Users/<user>/MyRepos/Scala/spark/python/pyspark/mllib/classification.py:docstring of pyspark.mllib.classification.LogisticRegressionWithSGD.train:16: WARNING: Definition list ends without a blank line; unexpected unindent.
/Users/<user>/MyRepos/Scala/spark/python/pyspark/mllib/classification.py:docstring of pyspark.mllib.classification.LogisticRegressionWithSGD.train:17: WARNING: Block quote ends without a blank line; unexpected unindent.
/Users/<user>/MyRepos/Scala/spark/python/pyspark/mllib/classification.py:docstring of pyspark.mllib.classification.SVMWithSGD.train:14: ERROR: Unexpected indentation.
/Users/<user>/MyRepos/Scala/spark/python/pyspark/mllib/classification.py:docstring of pyspark.mllib.classification.SVMWithSGD.train:16: WARNING: Definition list ends without a blank line; unexpected unindent.
/Users/<user>/MyRepos/Scala/spark/python/pyspark/mllib/classification.py:docstring of pyspark.mllib.classification.SVMWithSGD.train:17: WARNING: Block quote ends without a blank line; unexpected unindent.
/Users/<user>/MyRepos/Scala/spark/python/docs/pyspark.mllib.rst:50: WARNING: missing attribute mentioned in :members: or __all__: module pyspark.mllib.regression, attribute RidgeRegressionModelLinearRegressionWithSGD
/Users/<user>/MyRepos/Scala/spark/python/pyspark/mllib/tree.py:docstring of pyspark.mllib.tree.DecisionTreeModel.predict:3: ERROR: Unexpected indentation.
...
checking consistency... /Users/<user>/MyRepos/Scala/spark/python/docs/modules.rst:: WARNING: document isn't included in any toctree
...
copying static files... WARNING: html_static_path entry u'/Users/<user>/MyRepos/Scala/spark/python/docs/_static' does not exist
...
build succeeded, 12 warnings.
```

Author: cocoatomo <[email protected]>

Closes apache#2653 from cocoatomo/issues/3773-sphinx-build-warnings and squashes the following commits:

6f65661 [cocoatomo] [SPARK-3773][PySpark][Doc] Sphinx build warning

python/pyspark/context.py

@@ -410,6 +410,7 @@ def sequenceFile(self, path, keyClass=None, valueClass=None, keyConverter=None,
         Read a Hadoop SequenceFile with arbitrary key and value Writable class from HDFS,
         a local file system (available on all nodes), or any Hadoop-supported file system URI.
         The mechanism is as follows:
+
             1. A Java RDD is created from the SequenceFile or other InputFormat, and the key
                and value Writable classes
             2. Serialization is attempted via Pyrolite pickling

python/pyspark/mllib/classification.py

@@ -89,11 +89,14 @@ def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0,
         @param regParam:          The regularizer parameter (default: 1.0).
         @param regType:           The type of regularizer used for training
                                   our model.
-                                  Allowed values: "l1" for using L1Updater,
-                                                  "l2" for using
-                                                       SquaredL2Updater,
-                                                  "none" for no regularizer.
-                                  (default: "none")
+
+                                  :Allowed values:
+                                     - "l1" for using L1Updater
+                                     - "l2" for using SquaredL2Updater
+                                     - "none" for no regularizer
+
+                                     (default: "none")
+
         @param intercept:         Boolean parameter which indicates the use
                                   or not of the augmented representation for
                                   training data (i.e. whether bias features
@@ -158,11 +161,14 @@ def train(cls, data, iterations=100, step=1.0, regParam=1.0,
         @param initialWeights:    The initial weights (default: None).
         @param regType:           The type of regularizer used for training
                                   our model.
-                                  Allowed values: "l1" for using L1Updater,
-                                                  "l2" for using
-                                                       SquaredL2Updater,
-                                                  "none" for no regularizer.
-                                  (default: "none")
+
+                                  :Allowed values:
+                                     - "l1" for using L1Updater
+                                     - "l2" for using SquaredL2Updater,
+                                     - "none" for no regularizer.
+
+                                     (default: "none")
+
         @param intercept:         Boolean parameter which indicates the use
                                   or not of the augmented representation for
                                   training data (i.e. whether bias features

python/pyspark/mllib/regression.py

@@ -22,7 +22,7 @@
 from pyspark.mllib.linalg import SparseVector, _convert_to_vector
 from pyspark.serializers import PickleSerializer, AutoBatchedSerializer
 
-__all__ = ['LabeledPoint', 'LinearModel', 'LinearRegressionModel', 'RidgeRegressionModel'
+__all__ = ['LabeledPoint', 'LinearModel', 'LinearRegressionModel', 'RidgeRegressionModel',
            'LinearRegressionWithSGD', 'LassoWithSGD', 'RidgeRegressionWithSGD']
 
 
@@ -155,11 +155,14 @@ def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0,
         @param regParam:          The regularizer parameter (default: 1.0).
         @param regType:           The type of regularizer used for training
                                   our model.
-                                  Allowed values: "l1" for using L1Updater,
-                                                  "l2" for using
-                                                       SquaredL2Updater,
-                                                  "none" for no regularizer.
-                                  (default: "none")
+
+                                  :Allowed values:
+                                     - "l1" for using L1Updater,
+                                     - "l2" for using SquaredL2Updater,
+                                     - "none" for no regularizer.
+
+                                     (default: "none")
+
         @param intercept:         Boolean parameter which indicates the use
                                   or not of the augmented representation for
                                   training data (i.e. whether bias features

python/pyspark/mllib/tree.py

@@ -48,6 +48,7 @@ def __del__(self):
     def predict(self, x):
         """
         Predict the label of one or more examples.
+
         :param x:  Data point (feature vector),
                    or an RDD of data points (feature vectors).
         """

python/pyspark/rdd.py

@@ -1208,6 +1208,7 @@ def saveAsSequenceFile(self, path, compressionCodecClass=None):
         Output a Python RDD of key-value pairs (of form C{RDD[(K, V)]}) to any Hadoop file
         system, using the L{org.apache.hadoop.io.Writable} types that we convert from the
         RDD's key and value types. The mechanism is as follows:
+
             1. Pyrolite is used to convert pickled Python RDD into RDD of Java objects.
             2. Keys and values of this Java RDD are converted to Writables and written out.