feat(model): introduce dynamic training flag for model forward pass

layers/SeasonPatch.py

@@ -98,7 +98,7 @@ class SeasonPatch(nn.Module):
         else:
             raise ValueError(f"Unsupported encoder_type: {encoder_type}")
 
-    def forward(self, x ):
+    def forward(self, x, training):
         # x: [B, L, C]
         x = x.permute(0, 2, 1)  # x: [B, C, L]
 
@@ -114,7 +114,7 @@ class SeasonPatch(nn.Module):
             z = z.permute(0, 1, 3, 2)  # z: [B, C, patch_num, d_model]
 
             # Cross-channel mixing
-            z_mix = self.mixer(z)  # z_mix: [B, C, patch_num, d_model]
+            z_mix = self.mixer(z, training)  # z_mix: [B, C, patch_num, d_model]
 
             # Flatten and predict
             z_mix = z_mix.view(B, C, N * D)  # z_mix: [B, C, patch_num * d_model]