class Embedding(nn.Module):
    def __init__(self, embedding_matrix):
        super(Embedding, self).__init__()

        self.embedding = nn.Embedding(embedding_matrix.shape[0], EMBED_SIZE * 2)
        self.embedding.weight = nn.Parameter(
            torch.tensor(embedding_matrix, dtype=torch.float32)
        )
        self.embedding.weight.requires_grad = False
        self.embedding_dropout = SpatialDropout(0.3)

    def forward(self, x):
        x = self.embedding(x)
        x = self.embedding_dropout(x)
        return x

class Encoder(nn.Module):
    def __init__(self, num_sub_targets):
        super(Encoder, self).__init__()

        self.lstm_1 = nn.LSTM(
            EMBED_SIZE * 2, HIDDEN_SIZE, bidirectional=True, batch_first=True
        )
        self.lstm_2 = nn.LSTM(
            HIDDEN_SIZE * 2, HIDDEN_SIZE, bidirectional=True, batch_first=True
        )