Add inits and explicit gradient test.

cmdstanpy/model.py

@@ -2221,6 +2221,8 @@ def diagnose(
             * "model": Gradients evaluated using autodiff.
             * "finite_diff": Gradients evaluated using finite differences.
             * "error": Delta between autodiff and finite difference gradients.
+
+            Gradients are evaluated in the unconstrained space.
         """
 
         with temp_single_json(data) as _data, \
@@ -2237,7 +2239,7 @@ def diagnose(
             if _data is not None:
                 cmd += ["data", f"file={_data}"]
             if _inits is not None:
-                cmd.append(f"inits={_inits}")
+                cmd.append(f"init={_inits}")
 
             output_dir = tempfile.mkdtemp(prefix=self.name, dir=_TMPDIR)
 

test/test_model.py

@@ -613,6 +613,11 @@ def test_diagnose():
         "error",
     }
 
+    # Check gradients against the same value as in `log_prob`.
+    inits = {"theta": 0.34903938392023830482}
+    gradients = model.diagnose(data=BERN_DATA, inits=inits)
+    np.testing.assert_allclose(gradients.model.iloc[0], -1.18847)
+
     # Simulate bad gradients by using large finite difference.
     with pytest.raises(RuntimeError, match="may exceed the error threshold"):
         model.diagnose(data=BERN_DATA, epsilon=3)