added disallow_untyped_calls to mypy config

eagerpy/modules.py

@@ -1,13 +1,13 @@
 from importlib import import_module
 import inspect
 from types import ModuleType
-from typing import Any
+from typing import Any, Callable
 import functools
 
 from .astensor import astensor
 
 
-def wrap(f):
+def wrap(f: Callable) -> Callable:
     @functools.wraps(f)
     def wrapper(*args, **kwargs):
         result = f(*args, **kwargs)
@@ -24,7 +24,7 @@ class ModuleWrapper(ModuleType):
     """A wrapper for modules that delays the import until it is needed
     and wraps the output of functions as EagerPy tensors"""
 
-    def __init__(self, *args, **kwargs):
+    def __init__(self, *args, **kwargs) -> None:
         super().__init__(*args, **kwargs)
         if self.__doc__ is None:
             self.__doc__ = f"EagerPy wrapper of the '{self.__name__}' module"

eagerpy/tensor/jax.py

@@ -61,7 +61,7 @@ def raw(self) -> "np.ndarray":  # type: ignore
         return super().raw
 
     @classmethod
-    def _get_subkey(cls):
+    def _get_subkey(cls) -> Any:
         if cls.key is None:
             cls.key = jax.random.PRNGKey(0)
         cls.key, subkey = jax.random.split(cls.key)

eagerpy/tensor/pytorch.py

@@ -328,9 +328,9 @@ def flip(self: TensorType, axis=None) -> TensorType:
     def meshgrid(self: TensorType, *tensors, indexing="xy") -> Tuple[TensorType, ...]:
         tensors = unwrap_(tensors)
         if indexing == "ij" or len(tensors) == 0:
-            outputs = torch.meshgrid(self.raw, *tensors)
+            outputs = torch.meshgrid(self.raw, *tensors)  # type: ignore
         elif indexing == "xy":
-            outputs = torch.meshgrid(tensors[0], self.raw, *tensors[1:])
+            outputs = torch.meshgrid(tensors[0], self.raw, *tensors[1:])  # type: ignore
         else:
             raise ValueError(  # pragma: no cover
                 f"Valid values for indexing are 'xy' and 'ij', got {indexing}"
@@ -365,7 +365,7 @@ def isnan(self: TensorType) -> TensorType:
         return type(self)(torch.isnan(self.raw))
 
     def isinf(self: TensorType) -> TensorType:
-        return type(self)(torch.isinf(self.raw))
+        return type(self)(torch.isinf(self.raw))  # type: ignore
 
     def crossentropy(self: TensorType, labels: TensorType) -> TensorType:
         if self.ndim != 2:

setup.cfg

@@ -13,6 +13,7 @@ warn_redundant_casts = True
 warn_unreachable = True
 ignore_missing_imports = False
 disallow_any_unimported = True
+disallow_untyped_calls = True
 
 [mypy-numpy.*]
 ignore_missing_imports = True

tests/test_main.py

@@ -3,6 +3,7 @@
 import numpy as np
 import eagerpy as ep
 from eagerpy import Tensor
+from typing import Callable
 
 
 # make sure there are no undecorated tests in the "special tests" section below
@@ -344,10 +345,10 @@ def test_fn(*args, **kwargs):
     return test_fn
 
 
-def compare_allclose(*args, rtol=1e-07, atol=0):
+def compare_allclose(*args, rtol: float = 1e-07, atol: float = 0):
     """A decorator to simplify writing test functions"""
 
-    def compare_allclose_inner(f):
+    def compare_allclose_inner(f: Callable) -> Callable:
         @functools.wraps(f)
         def test_fn(*args, **kwargs):
             assert len(args) == 0