From 8cf60bbeb19dc51beb2bf698fffed49f7ac96b79 Mon Sep 17 00:00:00 2001
From: kukirin-ui <325067280+kukirin-ui@users.noreply.github.com>
Date: Fri, 16 Oct 2026 14:45:55 +0200
Subject: [PATCH] ty: un-ignore call-non-callable rule

---
 .../back_propagation_neural_network.py        | 19 +++++++++++++++----
 pyproject.toml                                |  1 -
 2 files changed, 15 insertions(+), 5 deletions(-)

diff --git a/neural_network/back_propagation_neural_network.py b/neural_network/back_propagation_neural_network.py
index 04fd7be84331..f9e2d691a1a0 100644
--- a/neural_network/back_propagation_neural_network.py
+++ b/neural_network/back_propagation_neural_network.py
@@ -18,6 +18,8 @@
 
 """
 
+from collections.abc import Callable
+
 import numpy as np
 from matplotlib import pyplot as plt
 
@@ -32,18 +34,29 @@ class DenseLayer:
     """
 
     def __init__(
-        self, units, activation=None, learning_rate=None, is_input_layer=False
+        self,
+        units,
+        activation: Callable[[np.ndarray], np.ndarray] | None = None,
+        learning_rate=None,
+        is_input_layer=False,
     ) -> None:
         """
         common connected layer of bp network
         :param units: numbers of neural units
-        :param activation: activation function
+        :param activation: activation function, sigmoid if not given
         :param learning_rate: learning rate for paras
         :param is_input_layer: whether it is input layer or not
+
+        >>> DenseLayer(3).activation is sigmoid
+        True
+        >>> DenseLayer(3, activation=np.tanh).activation is np.tanh
+        True
         """
         self.units = units
         self.weight = None
         self.bias = None
+        if activation is None:
+            activation = sigmoid
         self.activation = activation
         if learning_rate is None:
             learning_rate = 0.3
@@ -54,8 +67,6 @@ def initializer(self, back_units) -> None:
         rng = np.random.default_rng()
         self.weight = np.asmatrix(rng.normal(0, 0.5, (self.units, back_units)))
         self.bias = np.asmatrix(rng.normal(0, 0.5, self.units)).T
-        if self.activation is None:
-            self.activation = sigmoid
 
     def cal_gradient(self):
         # activation function may be sigmoid or linear
diff --git a/pyproject.toml b/pyproject.toml
index 932f64f2e00a..b2cef20eb68a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -316,7 +316,6 @@ skip = """\
 # assumptions consistent with the CI job in .github/workflows/ty.yml, where the
 # free-threaded 3.15t test venv would otherwise leave several stubs unresolved.
 environment.python-version = "3.15"
-rules.call-non-callable = "ignore"
 rules.invalid-argument-type = "ignore"
 rules.invalid-return-type = "ignore"
 rules.no-matching-overload = "ignore"