class LSTMTextGenerator(nn.Module, PyTorchModelHubMixin):
    def __init__(self, input_size=45, hidden_size=512, output_size=45, num_layers=2, dropout=0.5):
        super(LSTMTextGenerator, self).__init__()
        self.embedding = nn.Embedding(input_size, hidden_size)
        self.lstm = nn.LSTM(hidden_size, hidden_size, num_layers, batch_first=True, dropout=dropout, bidirectional=False)
        self.fc = nn.Linear(hidden_size, output_size)
        self.num_layers = num_layers
        self.hidden_size = hidden_size

    def forward(self, x, hidden):
        x = x.to(torch.long)
        x = self.embedding(x)
        x, hidden = self.lstm(x, hidden)
        x = self.fc(x)
        return x, hidden

    def init_hidden(self, batch_size):
        return (torch.zeros(self.num_layers, batch_size, self.hidden_size).to(device),
                torch.zeros(self.num_layers, batch_size, self.hidden_size).to(device))
        

class PreTrainedPipeline():
    def __init__(self, path=""):
        self.model = model = LSTMTextGenerator.from_pretrained("miittnnss/lstm-textgen-pets")
 
    def __call__(self, inputs: str):
        """
        Args:
            inputs (:obj:`str`):
                a string containing some text
        Return:
            A :obj:`PIL.Image` with the raw image representation as PIL.
        """
        # IMPLEMENT_THIS
        raise NotImplementedError(
            "Please implement PreTrainedPipeline __call__ function"
        )