diff --git a/tutorials/02-intermediate/bidirectional_recurrent_neural_network/main.py b/tutorials/02-intermediate/bidirectional_recurrent_neural_network/main.py
index a0ecd773..ef892893 100644
--- a/tutorials/02-intermediate/bidirectional_recurrent_neural_network/main.py
+++ b/tutorials/02-intermediate/bidirectional_recurrent_neural_network/main.py
@@ -53,8 +53,12 @@ def forward(self, x):
         # Forward propagate LSTM
         out, _ = self.lstm(x, (h0, c0))  # out: tensor of shape (batch_size, seq_length, hidden_size*2)
         
+        out1, out2 = torch.chunk(out, 2, dim=2)
+        out_cat = torch.cat((out1[:, -1, :], out2[:, 0, :]), 1)
+
         # Decode the hidden state of the last time step
-        out = self.fc(out[:, -1, :])
+        out = self.fc(out_cat)
+        
         return out
 
 model = BiRNN(input_size, hidden_size, num_layers, num_classes).to(device)
@@ -99,4 +103,4 @@ def forward(self, x):
     print('Test Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total)) 
 
 # Save the model checkpoint
-torch.save(model.state_dict(), 'model.ckpt')
\ No newline at end of file
+torch.save(model.state_dict(), 'model.ckpt')