Pytorch的torch.nn.embedding()如何实现词嵌入层

import torch
import torch.nn as nn


#词表
word_to_id = {'hello':0, '<PAD>':1,'world':2}
embeds = nn.Embedding(len(word_to_id), 4,padding_idx=word_to_id['<PAD>'])

text = 'hello world <PAD> <PAD>'
hello_idx = torch.LongTensor([word_to_id[i] for i in text.split()])
#词嵌入得到词向量
hello_embed = embeds(hello_idx)
print(hello_embed)

class Network(nn.Module):
    def __init__(self):
        super(TextCNN, self).__init__(nvocab,embed)
        self.filter_sizes = (2, 3, 4)
        self.embed = embed
        self.num_filters = 256
        self.dropout = 0.5
        self.num_classes = num_classes
        self.n_vocab = nvocab
        #通过padding_idx将<PAD>字符填充为0，因为他没意义哦，后面max-pooling自然而然会把他过滤掉哦
        self.embedding = nn.Embedding(self.n_vocab, self.embed, padding_idx=word2idx['<PAD>'])
        self.convs = nn.ModuleList(
            [nn.Conv2d(1, self.num_filters, (k, self.embed)) for k in self.filter_sizes])
        
        self.dropout = nn.Dropout(self.dropout)
        self.fc = nn.Linear(self.num_filters * len(self.filter_sizes), self.num_classes)
        
    def conv_and_pool(self, x, conv):
        x = F.relu(conv(x)).squeeze(3)
        x = F.max_pool1d(x, x.size(2)).squeeze(2)
        return x
        
    def forward(self, x):
        out = self.embedding(x)
        out = out.unsqueeze(1)
        out = torch.cat([self.conv_and_pool(out, conv) for conv in self.convs], 1)
        out = self.dropout(out)
        out = self.fc(out)
        return out