diff --git a/ax/utils/measurement/synthetic_functions.py b/ax/utils/measurement/synthetic_functions.py
index 34142101047..893654ee88f 100644
--- a/ax/utils/measurement/synthetic_functions.py
+++ b/ax/utils/measurement/synthetic_functions.py
@@ -14,11 +14,11 @@
 from ax.utils.common.docutils import copy_doc
 from ax.utils.common.typeutils import checked_cast, not_none
 from botorch.test_functions import synthetic as botorch_synthetic
+from pyre_extensions import override
 
 
-# pyre-fixme[3]: Return annotation cannot be `Any`.
 # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
-def informative_failure_on_none(func: Callable) -> Any:
+def informative_failure_on_none(func: Callable) -> Callable:
     # pyre-fixme[3]: Return annotation cannot be `Any`.
     # pyre-fixme[2]: Parameter must be annotated.
     def function_wrapper(*args, **kwargs) -> Any:
@@ -34,8 +34,7 @@ def function_wrapper(*args, **kwargs) -> Any:
 
 class SyntheticFunction(ABC):
 
-    # pyre-fixme[4]: Attribute must be annotated.
-    _required_dimensionality = None
+    _required_dimensionality: Optional[int] = None
     # pyre-fixme[4]: Attribute must be annotated.
     _domain = None
     # pyre-fixme[4]: Attribute must be annotated.
@@ -165,17 +164,16 @@ def fmax(self) -> float:
         """Value at global minimum(s)."""
         return self._fmax
 
-    @classmethod
     @abstractmethod
     def _f(self, X: np.ndarray) -> float:
         """Implementation of the synthetic function. Must be implemented in subclass.
 
         Args:
-            X (numpy.ndarray): an n by d array, where n represents the number
-                of observations and d is the dimensionality of the inputs.
+            X: A one-dimensional array with `d` elements, where d is the
+                dimensionality of the inputs.
 
         Returns:
-            numpy.ndarray: an n-dimensional array.
+            float: Function value.
         """
         ...
 
@@ -185,17 +183,18 @@ def __init__(
         self, botorch_synthetic_function: botorch_synthetic.SyntheticTestFunction
     ) -> None:
         self._botorch_function = botorch_synthetic_function
-        # pyre-fixme[4]: Attribute must be annotated.
-        self._required_dimensionality = self._botorch_function.dim
-        # pyre-fixme[4]: Attribute must be annotated.
-        self._domain = self._botorch_function._bounds
-        # pyre-fixme[4]: Attribute must be annotated.
-        self._fmin = self._botorch_function._optimal_value
+        self._required_dimensionality: int = self._botorch_function.dim
+        self._domain: Optional[List[Tuple[float, float]]] = (
+            self._botorch_function._bounds
+        )
+        self._fmin: float = self._botorch_function._optimal_value
 
+    @override
     @property
     def name(self) -> str:
         return f"{self.__class__.__name__}_{self._botorch_function.__class__.__name__}"
 
+    @override
     def _f(self, X: np.ndarray) -> float:
         # TODO: support batch evaluation
         return float(self._botorch_function(X=torch.from_numpy(X)).item())
@@ -212,16 +211,12 @@ class Hartmann6(SyntheticFunction):
     """Hartmann6 function (6-dimensional with 1 global minimum)."""
 
     _required_dimensionality = 6
-    # pyre-fixme[4]: Attribute must be annotated.
-    _domain = [(0, 1) for i in range(6)]
+    _domain: List[Tuple[int, int]] = [(0, 1) for i in range(6)]
     _minimums = [(0.20169, 0.150011, 0.476874, 0.275332, 0.311652, 0.6573)]
-    # pyre-fixme[4]: Attribute must be annotated.
-    _fmin = -3.32237
+    _fmin: float = -3.32237
     _fmax = 0.0
-    # pyre-fixme[4]: Attribute must be annotated.
-    _alpha = np.array([1.0, 1.2, 3.0, 3.2])
-    # pyre-fixme[4]: Attribute must be annotated.
-    _A = np.array(
+    _alpha: np.ndarray = np.array([1.0, 1.2, 3.0, 3.2])
+    _A: np.ndarray = np.array(
         [
             [10, 3, 17, 3.5, 1.7, 8],
             [0.05, 10, 17, 0.1, 8, 14],
@@ -229,8 +224,7 @@ class Hartmann6(SyntheticFunction):
             [17, 8, 0.05, 10, 0.1, 14],
         ]
     )
-    # pyre-fixme[4]: Attribute must be annotated.
-    _P = 10 ** (-4) * np.array(
+    _P: np.ndarray = 10 ** (-4) * np.array(
         [
             [1312, 1696, 5569, 124, 8283, 5886],
             [2329, 4135, 8307, 3736, 1004, 9991],
@@ -239,6 +233,7 @@ class Hartmann6(SyntheticFunction):
         ]
     )
 
+    @override
     @copy_doc(SyntheticFunction._f)
     def _f(self, X: np.ndarray) -> float:
         y = 0.0
@@ -254,15 +249,14 @@ class Aug_Hartmann6(Hartmann6):
     """Augmented Hartmann6 function (7-dimensional with 1 global minimum)."""
 
     _required_dimensionality = 7
-    # pyre-fixme[4]: Attribute must be annotated.
-    _domain = [(0, 1) for i in range(7)]
+    _domain: List[Tuple[int, int]] = [(0, 1) for i in range(7)]
     # pyre-fixme[15]: `_minimums` overrides attribute defined in `Hartmann6`
     #  inconsistently.
     _minimums = [(0.20169, 0.150011, 0.476874, 0.275332, 0.311652, 0.6573, 1.0)]
-    # pyre-fixme[4]: Attribute must be annotated.
-    _fmin = -3.32237
+    _fmin: float = -3.32237
     _fmax = 0.0
 
+    @override
     @copy_doc(SyntheticFunction._f)
     def _f(self, X: np.ndarray) -> float:
         y = 0.0
@@ -283,11 +277,15 @@ class Branin(SyntheticFunction):
 
     _required_dimensionality = 2
     _domain = [(-5, 10), (0, 15)]
-    # pyre-fixme[4]: Attribute must be annotated.
-    _minimums = [(-np.pi, 12.275), (np.pi, 2.275), (9.42478, 2.475)]
+    _minimums: List[Tuple[float, float]] = [
+        (-np.pi, 12.275),
+        (np.pi, 2.275),
+        (9.42478, 2.475),
+    ]
     _fmin = 0.397887
     _fmax = 308.129
 
+    @override
     @copy_doc(SyntheticFunction._f)
     def _f(self, X: np.ndarray) -> float:
         x_1 = X[0]
@@ -304,11 +302,15 @@ class Aug_Branin(SyntheticFunction):
 
     _required_dimensionality = 3
     _domain = [(-5, 10), (0, 15), (0, 1)]
-    # pyre-fixme[4]: Attribute must be annotated.
-    _minimums = [(-np.pi, 12.275, 1), (np.pi, 2.275, 1), (9.42478, 2.475, 1)]
+    _minimums: List[Tuple[float, float, int]] = [
+        (-np.pi, 12.275, 1),
+        (np.pi, 2.275, 1),
+        (9.42478, 2.475, 1),
+    ]
     _fmin = 0.397887
     _fmax = 308.129
 
+    @override
     @copy_doc(SyntheticFunction._f)
     def _f(self, X: np.ndarray) -> float:
         x_1 = X[0]