Pass coords argument into as_model decorator

pymc_experimental/model/model_api.py

@@ -7,6 +7,7 @@ def as_model(*model_args, **model_kwargs):
     R"""
     Decorator to provide context to PyMC models declared in a function.
     This removes all need to think about context managers and lets you separate creating a generative model from using the model.
+    Additionally, a coords argument is added to the function so coords can be changed during function invocation
 
     Adapted from `Rob Zinkov's blog post <https://www.zinkov.com/posts/2023-alternative-frontends-pymc/>`_ and inspired by the `sampled <https://github.com/colcarroll/sampled>`_ decorator for PyMC3.
 
@@ -32,12 +33,21 @@ def basic_model():
         m = basic_model()
         pm.sample(model=m)
 
+        # alternative way to use functional API
+        @pmx.as_model()
+        def basic_model():
+            pm.Normal("x", 0., 1., dims="obs")
+
+        m = basic_model(coords={"obs": ["a", "b"]})
+        pm.sample(model=m)
+
     """
 
     def decorator(f):
         @wraps(f)
         def make_model(*args, **kwargs):
-            with Model(*model_args, **model_kwargs) as m:
+            coords = model_kwargs.pop("coords", {}) | kwargs.pop("coords", {})
+            with Model(*model_args, coords=coords, **model_kwargs) as m:
                 f(*args, **kwargs)
             return m
 

pymc_experimental/tests/model/test_model_api.py

@@ -19,4 +19,11 @@ def model_wrapped():
 
     mw = model_wrapped()
 
+    @pmx.as_model()
+    def model_wrapped2():
+        pm.Normal("x", 0.0, 1.0, dims="obs")
+
+    mw2 = model_wrapped2(coords=coords)
+
     np.testing.assert_equal(model.point_logps(), mw.point_logps())
+    np.testing.assert_equal(mw.point_logps(), mw2.point_logps())