Fix forward pass of BiRNN

tutorials/intermediate/bidirectional_recurrent_neural_network/src/bi_rnn.cpp

@@ -10,10 +10,20 @@ BiRNNImpl::BiRNNImpl(int64_t input_size, int64_t hidden_size, int64_t num_layers
 }
 
 torch::Tensor BiRNNImpl::forward(torch::Tensor x) {
-    auto out = lstm->forward(x)
-        .output
-        .slice(1, -1)
-        .squeeze(1);
-    out = fc->forward(out);
+    auto out = lstm->forward(x).output;  // out: tensor of shape (batch_size, sequence_length, 2 * hidden_size)
+
+    // Concatenate the last hidden state of forward LSTM and first hidden state of backward LSTM
+    //
+    // Source: Translated from python code at
+    // https://github.com/yunjey/pytorch-tutorial/pull/174/commits/8c0897ee93fed8d9b352d33a60c1f931c9be5351
+    auto out_directions = out.chunk(2, 2);
+    // Last hidden state of forward direction output
+    auto out_1 = out_directions[0].slice(1, -1).squeeze(1);  // out_1: tensor of shape (batch_size, hidden_size)
+    // First hidden state of backward direction output
+    auto out_2 = out_directions[1].slice(1, 0, 1).squeeze(1);  // out_2: tensor of shape (batch_size, hidden_size)
+    auto out_cat = torch::cat({out_1, out_2}, 1);  // out_cat: tensor of shape (batch_size, 2 * hidden_size)
+
+    out = fc->forward(out_cat);
     return torch::log_softmax(out, 1);
 }
+