refactor: Unnest nn.Sequential for simpler handling

The original hidden_layer in ResidualBlock consists of a nn.Linear and a nn.SiLU. Sperating them will affect nothing, but make the layer structure consistent with other child nodes output_layer and residual_layer

src/timesfm_torch/pytorch_patched_decoder.py

@@ -260,18 +260,16 @@ def __init__(
     self.output_dims = output_dims
 
     # Hidden Layer
-    self.hidden_layer = nn.Sequential(
-        nn.Linear(input_dims, hidden_dims),
-        nn.SiLU(),
-    )
-
+    self.hidden_layer = nn.Linear(input_dims, hidden_dims)
+    # Activation Function
+    self.act = nn.SiLU()
     # Output Layer
     self.output_layer = nn.Linear(hidden_dims, output_dims)
     # Residual Layer
     self.residual_layer = nn.Linear(input_dims, output_dims)
 
   def forward(self, x):
-    hidden = self.hidden_layer(x)
+    hidden = self.act(self.hidden_layer(x))
     output = self.output_layer(hidden)
     residual = self.residual_layer(x)
     return output + residual