Fix linting

Author: paddyroddy

src/array_api_extra/_delegation.py

@@ -118,11 +118,10 @@ def nan_to_num(
     /,
     *,
     fill_value: int | float | complex = 0.0,
-    xp: ModuleType | None = None
+    xp: ModuleType | None = None,
 ) -> Array:
     """
-    Replace NaN with zero and infinity with large finite numbers (default
-    behaviour).
+    Replace NaN with zero and infinity with large finite numbers (default behaviour).
 
     If `x` is inexact, NaN is replaced by zero or by the user defined value in
     `nan` keyword, infinity is replaced by the largest finite floating point
@@ -141,6 +140,8 @@ def nan_to_num(
     fill_value : int, float, complex, optional
         Value to be used to fill NaN values. If no value is passed
         then NaN values will be replaced with 0.0.
+    xp : array_namespace, optional
+        The standard-compatible namespace for `x`. Default: infer.
 
     Returns
     -------

src/array_api_extra/_lib/_funcs.py

@@ -738,17 +738,16 @@ def kron(
     return xp.reshape(result, res_shape)
 
 
-def nan_to_num(
+def nan_to_num(  # numpydoc ignore=PR01,RT01
     x: Array,
     /,
     fill_value: int | float | complex = 0.0,
     *,
     xp: ModuleType,
 ) -> Array:
     """See docstring in `array_api_extra._delegation.py`."""
-    xp = array_namespace(x) if xp is None else xp
 
-    def perform_replacements(
+    def perform_replacements(  # numpydoc ignore=PR01,RT01
         x: Array,
         fill_value: int | float | complex,
         xp: ModuleType,

tests/conftest.py

@@ -233,9 +233,10 @@ def device(
         return xp.device("cpu")
     return get_device(xp.empty(0))
 
+
 @pytest.fixture
 def infinity(library: Backend) -> float:
     """Retrieve the positive infinity value for the given backend."""
     if library in (Backend.TORCH, Backend.TORCH_GPU):
-        return 3.4028235e+38
-    return 1.7976931348623157e+308
+        return 3.4028235e38
+    return 1.7976931348623157e308

tests/test_funcs.py

@@ -985,13 +985,7 @@ def test_complex(self, xp: ModuleType, infinity: float) -> None:
         )
         xp_assert_equal(
             nan_to_num(a),
-            xp.asarray(
-                [
-                    infinity + 0j,
-                    0 + 0j,
-                    0 + 1j * infinity
-                ]
-            ),
+            xp.asarray([infinity + 0j, 0 + 0j, 0 + 1j * infinity]),
         )