```
def denoise_train(x: DataLoader):
loss = 0
x_padded = list(map(lambda s: pad_string(s), x))
x_idx_tensor = strings_to_index_tensor(x_padded)
noisy_x = list(map(lambda s: noise_name(s), x))
noisy_x_padded = list(map(lambda s: pad_string(s), noisy_x))
noisy_x_idx_tensor = strings_to_index_tensor(noisy_x_padded)
noisy_x_rnn_tensor = to_rnn_tensor(noisy_x_idx_tensor)
batch_sz = len(x)
encoder_hidden = encoder.init_hidden(batch_size=batch_sz)
for i in range(noisy_x_rnn_tensor.shape[0]):
_, encoder_hidden = encoder(noisy_x_rnn_tensor[i].unsqueeze(0), encoder_hidden)
decoder_input = strings_to_tensor([SOS] * batch_sz)
decoder_hidden = encoder_hidden
names = [''] * batch_sz
for i in range(x_idx_tensor.shape[0]):
decoder_probs, decoder_hidden = decoder(decoder_input, decoder_hidden)
nonzero_indexes = x_idx_tensor[i]
best_indexes = torch.squeeze(torch.argmax(decoder_probs, dim=2), dim=0)
decoder_probs = torch.squeeze(decoder_probs, dim=0)
best_chars = list(map(lambda idx: index_to_char(int(idx)), best_indexes))
loss += criterion(decoder_probs, nonzero_indexes.type(torch.LongTensor))
for i, char in enumerate(best_chars):
names[i] += char
decoder_input = strings_to_tensor(best_chars)
loss.backward()
return names, noisy_x, loss.item()
```

I have this code for a denoising autoencoder on first names. It takes in a first name then pre-pads it with PAD char which I denote. I set it up like this so I could do batch training, but after training it for a couple days, I ran a name through it and it just printed out pads, which makes sense because it backprops on every iteration of a char so it’s being rewarded for just generating pads. How do I set it up not to learn this? I need the padding for batch training, because the names aren’t all consistent length so the padding allows them to be a consistent length. Currently when I put a noised name through it after training it just puts out all pads