Fixes formatting for with_columns docs page

docs/reference/decorators/with_columns.rst

@@ -2,13 +2,18 @@
 with_columns
 =======================
 
-** Overview **
+
+--------
+Overview
+--------
 
 This is part of the hamilton pyspark integration. To install, run:
 
-`pip install sf-hamilton[pyspark]`
+``pip install sf-hamilton[pyspark]``
 
-**Reference Documentation**
+-----------------------
+Reference Documentation
+-----------------------
 
 .. autoclass:: hamilton.plugins.h_spark.with_columns
    :special-members: __init__

hamilton/plugins/h_spark.py

@@ -41,7 +41,7 @@ class SparkKoalasGraphAdapter(base.HamiltonGraphAdapter, base.ResultMixin):
     using the \
     `Pandas API on Spark <https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/index.html>`__
 
-    Use `pip install sf-hamilton[spark]` to get the dependencies required to run this.
+    Use ``pip install sf-hamilton[spark]`` to get the dependencies required to run this.
 
     Currently, this class assumes you're running SPARK 3.2+. You'd generally use this if you have an existing spark \
     cluster running in your workplace, and you want to scale to very large data set sizes.
@@ -759,7 +759,7 @@ def transform_node(
 
         Note that, at this point, we don't actually know which columns will come from the
         base dataframe, and which will come from the upstream nodes. This is handled in the
-        `with_columns` decorator, so for now, we need to give it enough information to topologically
+        ``with_columns`` decorator, so for now, we need to give it enough information to topologically
         sort/assign dependencies.
 
         :param node_: Node to transform
@@ -950,10 +950,10 @@ def __init__(
         """Initializes a with_columns decorator for spark. This allows you to efficiently run
          groups of map operations on a dataframe, represented as pandas/primitives UDFs. This
          effectively "linearizes" compute -- meaning that a DAG of map operations can be run
-         as a set of `.withColumn` operations on a single dataframe -- ensuring that you don't have
-         to do a complex `extract` then `join` process on spark, which can be inefficient.
+         as a set of ``.withColumn`` operations on a single dataframe -- ensuring that you don't have
+         to do a complex ``extract`` then ``join`` process on spark, which can be inefficient.
 
-         Here's an example of calling it -- if you've seen `@subdag`, you should be familiar with
+         Here's an example of calling it -- if you've seen :py:class:`@subdag <hamilton.function_modifiers.recursive>`, you should be familiar with
          the concepts:
 
          .. code-block:: python