mypy.

python-package/xgboost/dask.py

@@ -1005,7 +1005,7 @@ def _infer_direct_predict_output(
     if isinstance(data, DaskDMatrix):
         features = data.num_col()
     else:
-        features = data.shape[1]
+        features = data.shape[1]  # type:ignore
     rng = numpy.random.RandomState(1994)
     test_sample = rng.randn(1, features)
     if inplace:
@@ -1183,7 +1183,7 @@ def dispatched_predict(booster: Booster, part: Any) -> numpy.ndarray:
 
 def predict(                    # pylint: disable=unused-argument
     client: "distributed.Client",
-    model: Union[TrainReturnT, Booster],
+    model: Union[TrainReturnT, Booster, "distributed.Future"],
     data: Union[DaskDMatrix, _DaskCollection],
     output_margin: bool = False,
     missing: float = numpy.nan,
@@ -1283,7 +1283,7 @@ def mapped_predict(
 
 def inplace_predict(            # pylint: disable=unused-argument
     client: "distributed.Client",
-    model: Union[TrainReturnT, Booster],
+    model: Union[TrainReturnT, Booster, "distributed.Future"],
     data: _DaskCollection,
     iteration_range: Tuple[int, int] = (0, 0),
     predict_type: str = 'value',
@@ -1392,7 +1392,7 @@ async def _get_booster_future(self) -> "distributed.Future":
         # pylint: disable=access-member-before-definition
         if not hasattr(self, "_booster_future") or self._booster_future_id != id(booster):
             self._booster_future = await self.client.scatter(booster)
-            self._booster_future_id = id(booster)
+            self._booster_future_id: int = id(booster)
         return self._booster_future
 
     def __await__(self) -> Awaitable[Any]:
@@ -1401,7 +1401,7 @@ async def _() -> Awaitable[Any]:
             return self
         return self.client.sync(_).__await__()
 
-    def __getstate__(self):
+    def __getstate__(self) -> Dict:
         this = self.__dict__.copy()
         if "_client" in this.keys():
             del this["_client"]

tests/python/test_with_dask.py

@@ -1034,7 +1034,7 @@ def run_shap(self, X: Any, y: Any, params: Dict[str, Any], client: "Client") ->
         rows = X.shape[0]
         cols = X.shape[1]
 
-        def assert_shape(shape):
+        def assert_shape(shape: Tuple[int, ...]) -> None:
             assert shape[0] == rows
             if "num_class" in params.keys():
                 assert shape[1] == params["num_class"]