recently i started learning PyTorch, so i have a model that takes an input and guess the next word
class LSTM(nn.Module):
    def __init__(self, hidden_size, embedding_size, vocab_size):
        super(LSTM, self).__init__()
        self.hidden_size = hidden_size
        self.vocab_size = vocab_size
        
        self.embedding = nn.Embedding(vocab_size, embedding_size)
        self.lstm = nn.LSTM(embedding_size, hidden_size)
        self.out = nn.Linear(hidden_size, vocab_size)
        self.dropout = nn.Dropout(0.1)
        self.softmax = nn.LogSoftmax(dim=1)
        
    def forward(self, sentence):
        embeds = self.embedding(sentence) 
        lstm_out, _ = self.lstm(embeds)
        output = self.out(output)
        output = self.dropout(output)
        out = self.softmax(output)
        return out

batch_size = 20
input_size, output size = 32
vocab_size = 9882 #generated dynamically in original code
dataloader = DataLoader(dataset=dataset, batch_size=batch_size, shuffle=False, drop_last=True)

loss_function = nn.NLLLoss()

optimizer = optim.Adam(model.parameters(), lr=0.1)

def train(epoch):
    model.train()
    current_loss = 0

    for x_batch, y_batch in dataloader:
    
      model.zero_grad()
      
      pred = model(x_batch)

      loss = loss_function(pred, y_batch)

      current_loss += loss
      loss.backward()
      optimizer.step()


stacktrace
ValueError                                Traceback (most recent call last)
<ipython-input-544-31519a92b1e7> in <module>()
      2 
      3 for iter in range(1, n_iters + 1):
----> 4   output, loss = train(iter)
      5   current_loss += loss
      6   print_loss_total += loss

3 frames
<ipython-input-541-a72868643ddf> in train(epoch)
     15       pred = model(x_batch)
     16 
---> 17       loss = loss_function(pred, y_batch)
     18       print(loss, loss, 'loss')
     19       current_loss += loss

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    545             result = self._slow_forward(*input, **kwargs)
    546         else:
--> 547             result = self.forward(*input, **kwargs)
    548         for hook in self._forward_hooks.values():
    549             hook_result = hook(self, input, result)

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/loss.py in forward(self, input, target)
    202 
    203     def forward(self, input, target):
--> 204         return F.nll_loss(input, target, weight=self.weight, ignore_index=self.ignore_index, reduction=self.reduction)
    205 
    206 

/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py in nll_loss(input, target, weight, size_average, ignore_index, reduce, reduction)
   1832         if target.size()[1:] != input.size()[2:]:
   1833             raise ValueError('Expected target size {}, got {}'.format(
-> 1834                 out_size, target.size()))
   1835         input = input.contiguous().view(n, c, 1, -1)
   1836         target = target.contiguous().view(n, 1, -1)

ValueError: Expected target size (20, 9882), got torch.Size([20, 32])

I have a gut feeling this might be related to topk but i really have no clue what could be wrong.
Thanks and any help will be appreciated.