问hidden_dim和embed_size在LSTM中的含义是什么？

EN

    class SentimentNet(nn.Module):
        def __init__(self, vocab_size, output_size, embedding_dim, hidden_dim, n_layers, drop_prob=0.5):
        super(SentimentNet, self).__init__()
        self.output_size = output_size
        self.n_layers = n_layers
        self.hidden_dim = hidden_dim

        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.lstm = nn.LSTM(embedding_dim, hidden_dim, n_layers, dropout=drop_prob, batch_first=True)
        self.dropout = nn.Dropout(drop_prob)
        self.fc = nn.Linear(hidden_dim, output_size)
        self.sigmoid = nn.Sigmoid()

     vocab_size = len(word2idx) + 1
     output_size = 1
     embedding_dim = 400
     hidden_dim = 512
     n_layers = 2