(self, x)
return x_highway
Maps x_conv_out to x_highway
       # nn.Linear
      @param x (tensor): x_conv_out tensor of shape (b, word_embed_size), 
                         where b = batch size
      @returns x_highway (tensor): tenosor of shape (b, word_embed_size)
Maps x_conv_out to x_highway
       # nn.Linear
Maps x_conv_out to x_highway # nn . Linear
def forward(self, x):
      """Maps x_conv_out to x_highway
       # nn.Linear
      @param x (tensor): x_conv_out tensor of shape (b, word_embed_size), 
                         where b = batch size
      @returns x_highway (tensor): tenosor of shape (b, word_embed_size)
      """

      x_proj = F.relu(self.w_proj(x)) # (b, word_embed_size)
      x_gate = torch.sigmoid(self.w_gate(x)) # (b, word_embed_size)

      # element wise multiplication: * or mul()
      x_highway = x_gate * x_proj + (1 - x_gate) * x # (b, word_embed_size)

      return x_highway
def forward ( self , x ) : x_proj = F . relu ( self . w_proj ( x ) ) # (b, word_embed_size) x_gate = torch . sigmoid ( self . w_gate ( x ) ) # (b, word_embed_size) # element wise multiplication: * or mul() x_highway = x_gate * x_proj + ( 1 - x_gate ) * x # (b, word_embed_size) return x_highway