input: Write a python function to predict the sentiment of a given sentence using the SentimentRNN model.
  assert isinstance(predict_sentiment("I am so happy right now!"), torch.Tensor)
  assert predict_sentiment("I am so happy right now!").shape == torch.Size([1])
  assert isinstance(predict_sentiment("This is the worst day ever."), torch.Tensor)
output: import torch
  from torchtext.legacy.data import Field, LabelField, TabularDataset, BucketIterator
  
  def predict_sentiment(sentence, model, vocab, tokenizer, device):
      model.eval()
      tokens = tokenizer(sentence)
      indexed = [vocab.stoi[t] for t in tokens]
      tensor = torch.LongTensor(indexed).to(device)
      tensor = tensor.unsqueeze(1)
      prediction = torch.sigmoid(model(tensor))
      return prediction