class LSTMTagger([nn.Module](https://docs.pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module "torch.nn.Module")):

    def __init__(self, embedding_dim, hidden_dim, vocab_size, tagset_size):
        super([LSTMTagger](https://docs.pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module "torch.nn.Module"), self).__init__()
        self.hidden_dim = hidden_dim

        self.word_embeddings = [nn.Embedding](https://docs.pytorch.org/docs/stable/generated/torch.nn.Embedding.html#torch.nn.Embedding "torch.nn.Embedding")(vocab_size, embedding_dim)

        # The LSTM takes word embeddings as inputs, and outputs hidden states
        # with dimensionality hidden_dim.
        self.[lstm](https://docs.pytorch.org/docs/stable/generated/torch.nn.LSTM.html#torch.nn.LSTM "torch.nn.LSTM") = [nn.LSTM](https://docs.pytorch.org/docs/stable/generated/torch.nn.LSTM.html#torch.nn.LSTM "torch.nn.LSTM")(embedding_dim, hidden_dim)

        # The linear layer that maps from hidden state space to tag space
        self.hidden2tag = [nn.Linear](https://docs.pytorch.org/docs/stable/generated/torch.nn.Linear.html#torch.nn.Linear "torch.nn.Linear")(hidden_dim, tagset_size)

    def forward(self, sentence):
        embeds = self.word_embeddings(sentence)
        lstm_out, _ = self.[lstm](https://docs.pytorch.org/docs/stable/generated/torch.nn.LSTM.html#torch.nn.LSTM "torch.nn.LSTM")(embeds.view(len(sentence), 1, -1))
        tag_space = self.hidden2tag(lstm_out.view(len(sentence), -1))
        [tag_scores](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "torch.Tensor") = [F.log_softmax](https://docs.pytorch.org/docs/stable/generated/torch.nn.functional.log_softmax.html#torch.nn.functional.log_softmax "torch.nn.functional.log_softmax")(tag_space, dim=1)
        return [tag_scores](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "torch.Tensor")