[DOC] Writing the set_output docs page to document functionality

docs/source/set_output.rst

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+.. _set_output:
+
+==========
+skpro ``set_output`` API for Regression Models
+==========
+
+The following example will demonstrate how to use the ``set_output`` API
+to configure the output container of your probabilistic predictions. Currently
+for regression estimators, the following predict functions are supported:
+``predict_quantile``, ``predict_interval``, ``predict_var``, and ``predict``.
+
+Available ``mtypes`` include ``pd.DataFrame`` from the ``pandas`` library
+and ``pl.DataFrame`` from the ``polars`` library.
+
+We first load an sklearn dataset and import an skpro regression estimator.
+
+.. code-block :: python
+
+    #import our dataset
+    from sklearn.datasets import load_diabetes
+    from sklearn.model_selection import train_test_split
+
+    #import our estimator
+    from sklearn.linear_model import LinearRegression
+    from skpro.regression.residual import ResidualDouble
+
+    X, y = load_diabetes(return_X_y=True, as_frame=True)
+    X = X.iloc[:75]
+    y = y.iloc[:75]
+    y = pd.DataFrame(y)
+    X_train, X_test, y_train, _ = train_test_split(
+    X, y, test_size=0.33, random_state=42
+    )
+
+    estimator = ResidualDouble(LinearRegression())
+
+Next, we will call the ``set_output`` method built into the estimator.
+
+.. code-block :: python
+
+    estimator.set_output(transform = "polars")
+
+After we fit the model, we can then call the ``predict`` function and the
+output will automatically be converted into a polars DataFrame
+
+.. code-block :: python
+
+    estimator.fit(X_train, y_train)
+
+    estimator.predict(X_test)
+
+*output polars dataframe here*