parameters: (self, x)
return_statement: return x_highway
docstring: 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)
docstring_summary: Maps x_conv_out to x_highway
         # nn.Linear
docstring_tokens: Maps x_conv_out to x_highway # nn . Linear
function: 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
function_tokens: 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