Fix docstrings of transform.Interval to provide example of how to ins…

…tantiate it

pymc/distributions/transforms.py

@@ -28,7 +28,7 @@
     "RVTransform",
     "simplex",
     "logodds",
-    "interval",
+    "Interval",
     "log_exp_m1",
     "ordered",
     "log",
@@ -166,10 +166,47 @@ def log_jac_det(self, value, *inputs):
 Instantiation of :class:`aeppl.transforms.LogOddsTransform`
 for use in the ``transform`` argument of a random variable."""
 
-interval = IntervalTransform
-interval.__doc__ = """
-Instantiation of :class:`aeppl.transforms.IntervalTransform`
-for use in the ``transform`` argument of a random variable."""
+Interval = IntervalTransform
+Interval.__doc__ = """
+Uninstantiated :class:`aeppl.transforms.IntervalTransform`
+for use in the ``transform`` argument of a random variable.
+
+Interval transform requires a callable during initialization
+which takes takes the underlying random variable inputs and
+returns the lower and upper bounds. If lower or upper is `None`,
+the interval is unbounded on that side.
+
+.. attention::
+    The returned bounds cannot depend on other model variables that
+    are not part of the random variable inputs.
+
+Examples
+--------
+.. code-block:: python
+
+    import pymc as pm
+
+    # Create an interval transform between -1 and +1
+    def get_bounds(rng, size, dtype, loc, scale):
+        return -1, 1
+
+    interval = pm.transforms.Interval(get_bounds)
+
+    with pm.Model():
+        x = pm.Normal("x", transform=interval)
+
+.. code-block:: python
+    import pymc as pm
+
+    # Create an upper bound interval transform based on a distribution parameter
+    def get_bounds(rng, size, dtype, loc, scale):
+        return None, loc
+
+    interval = pm.transforms.Interval(get_bounds)
+
+    with pm.Model():
+        x = pm.Normal("x", transform=interval)
+"""
 
 log_exp_m1 = LogExpM1()
 log_exp_m1.__doc__ = """