fix self instance of beta

snntorch/_neurons/leakyparallel.py

@@ -146,8 +146,8 @@ def __init__(
         self.rnn = nn.RNN(input_size, hidden_size, num_layers=1, nonlinearity='relu', 
                           bias=bias, batch_first=False, dropout=dropout, device=device, dtype=dtype)
         self._beta_buffer
-        if beta is not None:
-            beta = beta.clamp(0, 1) 
+        if self.beta is not None:
+            self.beta = self.beta.clamp(0, 1) 
 
         if spike_grad is None:
             self.spike_grad = self.ATan.apply
@@ -164,17 +164,17 @@ def __init__(
             self.spike_grad = self._surrogate_bypass
 
         with torch.no_grad():
-            if beta is not None:
-                # Set all weights to the scalar value of beta
-                if isinstance(beta, float) or isinstance(beta, int):
-                    self.rnn.weight_hh_10.fill_(beta)
-                elif isinstance(beta, torch.Tensor) or isinstance(beta, torch.FloatTensor):
-                    if len(beta) == 1:
-                        self.rnn.weight_hh_10.fill_(beta)
-                elif len(beta) == hidden_size:
-                    # Replace each value with the corresponding value in beta
+            if self.beta is not None:
+                # Set all weights to the scalar value of self.beta
+                if isinstance(self.beta, float) or isinstance(self.beta, int):
+                    self.rnn.weight_hh_10.fill_(self.beta)
+                elif isinstance(self.beta, torch.Tensor) or isinstance(self.beta, torch.FloatTensor):
+                    if len(self.beta) == 1:
+                        self.rnn.weight_hh_10.fill_(self.beta)
+                elif len(self.beta) == hidden_size:
+                    # Replace each value with the corresponding value in self.beta
                     for i in range(hidden_size):
-                        self.rnn.weight_hh_l0.data[i].fill_(beta[i])
+                        self.rnn.weight_hh_l0.data[i].fill_(self.beta[i])
                 else:
                     raise ValueError("Beta must be either a single value or of length 'hidden_size'.")