diff --git a/neural_network/lstm.py b/neural_network/lstm.py
index b24894e78..9abd96053 100644
--- a/neural_network/lstm.py
+++ b/neural_network/lstm.py
@@ -97,32 +97,22 @@ class LSTM:
         Initialize the weights and biases for the LSTM network.
         """
 
-        self.wf = self.init_weights(
-            self.char_size + self.hidden_dim, self.hidden_dim
-        )
+        self.wf = self.init_weights(self.char_size + self.hidden_dim, self.hidden_dim)
         self.bf = np.zeros((self.hidden_dim, 1))
 
-        self.wi = self.init_weights(
-            self.char_size + self.hidden_dim, self.hidden_dim
-        )
+        self.wi = self.init_weights(self.char_size + self.hidden_dim, self.hidden_dim)
         self.bi = np.zeros((self.hidden_dim, 1))
 
-        self.wc = self.init_weights(
-            self.char_size + self.hidden_dim, self.hidden_dim
-        )
+        self.wc = self.init_weights(self.char_size + self.hidden_dim, self.hidden_dim)
         self.bc = np.zeros((self.hidden_dim, 1))
 
-        self.wo = self.init_weights(
-            self.char_size + self.hidden_dim, self.hidden_dim
-        )
+        self.wo = self.init_weights(self.char_size + self.hidden_dim, self.hidden_dim)
         self.bo = np.zeros((self.hidden_dim, 1))
 
         self.wy = self.init_weights(self.hidden_dim, self.char_size)
         self.by = np.zeros((self.char_size, 1))
 
-    def init_weights(
-        self, input_dim: int, output_dim: int
-    ) -> np.ndarray:
+    def init_weights(self, input_dim: int, output_dim: int) -> np.ndarray:
         """
         Initialize weights with random values.
 
@@ -367,7 +357,6 @@ class LSTM:
         print(f"Accuracy: {round(accuracy * 100 / len(self.train_X), 2)}%")
 
 
-
 if __name__ == "__main__":
     data = """Long Short-Term Memory (LSTM) networks are a type
          of recurrent neural network (RNN) capable of learning "