original_docstring: 
          Encode embeddings, using dilated convolutional networks.
          Here, we suppose the total number of sentences in bags is batch_size.
  
          Args:
              embeddings: [batch_size, num_step, embedding_size]
          Return:
              hidden state of each sentence: [batch_size, hidden_size]
          
docstring: Encode embeddings, using dilated convolutional networks.
  Here, we suppose the total number of sentences in bags is batch_size.
docstring_tokens: Encode embeddings using dilated convolutional networks . Here we suppose the total number of sentences in bags is batch_size .
code: def forward(self, embeddings):
          """
          Encode embeddings, using dilated convolutional networks.
          Here, we suppose the total number of sentences in bags is batch_size.
  
          Args:
              embeddings: [batch_size, num_step, embedding_size]
          Return:
              hidden state of each sentence: [batch_size, hidden_size]
          """
  
          # embeddings = torch.unsqueeze(embeddings, dim=1)
          embeddings.unsqueeze_(dim=1)
  
          dilated_input = self.cnn(embeddings)
          h, _ = torch.max(dilated_input, dim=2)
  
          block_out = self.dilated_cnn(dilated_input)
          block_out, _ = torch.max(block_out, dim=2)
          h += block_out
  
          x, _ = torch.max(h, dim=2)
          x = x.view(-1, self.opt.hidden_size)
          return self.dropout(self.activation(x))
code_tokens: def forward ( self , embeddings ) : embeddings . unsqueeze_ ( dim = 1 ) dilated_input = self . cnn ( embeddings ) h , _ = torch . max ( dilated_input , dim = 2 ) block_out = self . dilated_cnn ( dilated_input ) block_out , _ = torch . max ( block_out , dim = 2 ) h += block_out x , _ = torch . max ( h , dim = 2 ) x = x . view ( - 1 , self . opt . hidden_size ) return self . dropout ( self . activation ( x ) )
short_docstring: Encode embeddings, using dilated convolutional networks.
short_docstring_tokens: Encode embeddings using dilated convolutional networks .
comment: """
          Encode embeddings, using dilated convolutional networks.
          Here, we suppose the total number of sentences in bags is batch_size.
  
          Args:
              embeddings: [batch_size, num_step, embedding_size]
          Return:
              hidden state of each sentence: [batch_size, hidden_size]
          """$SEP$# embeddings = torch.unsqueeze(embeddings, dim=1)