I am currently trying to implement a Seq2Seq model for machine translation task. I was following the an official Pytorch tutorial.

Tutorial Link

My theoretical understanding for the encoder was that it takes a single input at each timestamps and generates a new hidden state and the process continues i.e. taking the new word and previous hidden state. But in the tutorial they are passing all the input together to the encoder. How is the encoder able to understand the relationship between words if we do this?

I have attached the code snippet below

```
class EncoderRNN(nn.Module):
def __init__(self, input_size, hidden_size, dropout_p=0.1):
super(EncoderRNN, self).__init__()
self.hidden_size = hidden_size
self.embedding = nn.Embedding(input_size, hidden_size)
self.gru = nn.GRU(hidden_size, hidden_size, batch_first=True)
self.dropout = nn.Dropout(dropout_p)
def forward(self, input):
embedded = self.dropout(self.embedding(input))
output, hidden = self.gru(embedded)
return output, hidden
def train_epoch(dataloader, encoder, decoder, encoder_optimizer,
decoder_optimizer, criterion):
total_loss = 0
for data in dataloader:
input_tensor, target_tensor = data
encoder_optimizer.zero_grad()
decoder_optimizer.zero_grad()
encoder_outputs, encoder_hidden = encoder(input_tensor) # This line here
decoder_outputs, _, _ = decoder(encoder_outputs, encoder_hidden, target_tensor)
loss = criterion(
decoder_outputs.view(-1, decoder_outputs.size(-1)),
target_tensor.view(-1)
)
loss.backward()
encoder_optimizer.step()
decoder_optimizer.step()
total_loss += loss.item()
return total_loss / len(dataloader)
```