_ init _（）函数

参数： self, config, pretrained_word_embedding, pretrained_entity_embedding, pretrained_context_embedding
config：设置的固定的参数！
pretrained_word_embedding：根据下面的使用是一个bool类型，表示是不是单词被预训练过！
pretrained_entity_embedding、pretrained_context_embedding：同理也是

super(KCNN, self).__init__()   #调用父类（也就是torch.nn.Module）的初始化函数,建立继承关系
self.config = config           #config是预定义的超参数集合！ 各种配置文件都在这里面
if pretrained_word_embedding is None:  #如果没有预训练词典，那么就用第一个
           self.word_embedding = nn.Embedding(config.num_words,
                                              config.word_embedding_dim,
                                              padding_idx=0)
else:  #我们就用预训练的词典
     self.word_embedding = nn.Embedding.from_pretrained(
         pretrained_word_embedding, freeze=False, padding_idx=0)
if pretrained_entity_embedding is None:
     self.entity_embedding = nn.Embedding(config.num_entities,
                                          config.entity_embedding_dim,
                                          padding_idx=0)
else: #实体的嵌入也是一样的，同上
     self.entity_embedding = nn.Embedding.from_pretrained(
         pretrained_entity_embedding, freeze=False, padding_idx=0)
if config.use_context:   #上下文嵌入也是一样的，同上
    if pretrained_context_embedding is None:
        self.context_embedding = nn.Embedding(
            config.num_entities,
            config.entity_embedding_dim,
            padding_idx=0)
    else:
        self.context_embedding = nn.Embedding.from_pretrained(
            pretrained_context_embedding, freeze=False, padding_idx=0)
            
self.transform_matrix = nn.Parameter(   #确定transform的参数矩阵
        torch.empty(self.config.entity_embedding_dim,
                    self.config.word_embedding_dim).uniform_(-0.1, 0.1))
self.transform_bias = nn.Parameter(      #确定transform的偏置的参数矩阵
    torch.empty(self.config.word_embedding_dim).uniform_(-0.1, 0.1))

#下面是定义一个模块字典， 字典通过x也就是模块的大小来访问是哪个卷积！ 
self.conv_filters = nn.ModuleDict({    #(3/2, num_filters, (x, word_embedding_dim))
            str(x): nn.Conv2d(3 if self.config.use_context else 2,
                              self.config.num_filters,
                              (x, self.config.word_embedding_dim))
            for x in self.config.window_sizes
        })
self.additive_attention = AdditiveAttention(
	self.config.query_vector_dim, self.config.num_filters)

   def forward(self, news):
        """
        Args:
          news:       #输入的news参数是个字典！ （title， title_entities）  个数
            {
                "title": batch_size * num_words_title,
                "title_entities": batch_size * num_words_title
            }

        Returns:
            final_vector: batch_size, len(window_sizes) * num_filters
        """
        # batch_size, num_words_title, word_embedding_dim
        word_vector = self.word_embedding(news["title"].to(device))    #获取新闻中的单词向量
        # batch_size, num_words_title, entity_embedding_dim
        entity_vector = self.entity_embedding(                         #获取新闻中的实体向量
            news["title_entities"].to(device))
        if self.config.use_context:
            # batch_size, num_words_title, entity_embedding_dim
            context_vector = self.context_embedding(               #获取新闻中上下文向量（也就是关系向量）
                news["title_entities"].to(device))

        # batch_size, num_words_title, word_embedding_dim
        transformed_entity_vector = torch.tanh(
            torch.add(torch.matmul(entity_vector, self.transform_matrix),
                      self.transform_bias))

        if self.config.use_context:                          
            # batch_size, num_words_title, word_embedding_dim
            transformed_context_vector = torch.tanh(       #将上下文向量经过transform
                torch.add(torch.matmul(context_vector, self.transform_matrix),
                          self.transform_bias))

            # batch_size, 3, num_words_title, word_embedding_dim
            multi_channel_vector = torch.stack([          #将三个向量进行concat
                word_vector, transformed_entity_vector,
                transformed_context_vector
            ],
                                               dim=1)
        else:
            # batch_size, 2, num_words_title, word_embedding_dim
            multi_channel_vector = torch.stack(    #否则直接进行concat
                [word_vector, transformed_entity_vector], dim=1)

        pooled_vectors = []
        for x in self.config.window_sizes:       #进行预先设定好的，根据窗口大小进行操作！ 
            # batch_size, num_filters, num_words_title + 1 - x
            convoluted = self.conv_filters[str(x)](
                multi_channel_vector).squeeze(dim=3)
            # batch_size, num_filters, num_words_title + 1 - x
            activated = F.relu(convoluted)
            # batch_size, num_filters
            # Here we use a additive attention module
            # instead of pooling in the paper
            pooled = self.additive_attention(activated.transpose(1, 2))
            # pooled = activated.max(dim=-1)[0]
            # # or
            # # pooled = F.max_pool1d(activated, activated.size(2)).squeeze(dim=2)
            pooled_vectors.append(pooled)
        # batch_size, len(window_sizes) * num_filters
        final_vector = torch.cat(pooled_vectors, dim=1)   # 最终的向量是需要concat的！
        return final_vector