(self, static_ids)
return concat_embeddings
input: relative id
        static_ids: Batch_size * Field_size
        return: Batch_size * Field_size * Embedding_size
input: relative id
        static_ids: Batch_size * Field_size
        return: Batch_size * Field_size * Embedding_size
input : relative id static_ids : Batch_size * Field_size return : Batch_size * Field_size * Embedding_size
def forward(self, static_ids):
        """
        input: relative id
        static_ids: Batch_size * Field_size
        return: Batch_size * Field_size * Embedding_size
        """
        concat_embeddings = []
        batch_size = static_ids.shape[0]
        for i, key in enumerate(self.col_names):
            # B*1
            static_ids_tensor = torch.LongTensor(static_ids[key].values.astype(int))
            if self.use_cuda:
                static_ids_tensor = static_ids_tensor.cuda()

            static_embeddings_tensor = self.embeddings[i](static_ids_tensor)

            concat_embeddings.append(static_embeddings_tensor.view(batch_size, 1, self.embedding_size))
        # B*F*E
        concat_embeddings = torch.cat(concat_embeddings, 1)

        return concat_embeddings
def forward ( self , static_ids ) : concat_embeddings = [ ] batch_size = static_ids . shape [ 0 ] for i , key in enumerate ( self . col_names ) : # B*1 static_ids_tensor = torch . LongTensor ( static_ids [ key ] . values . astype ( int ) ) if self . use_cuda : static_ids_tensor = static_ids_tensor . cuda ( ) static_embeddings_tensor = self . embeddings [ i ] ( static_ids_tensor ) concat_embeddings . append ( static_embeddings_tensor . view ( batch_size , 1 , self . embedding_size ) ) # B*F*E concat_embeddings = torch . cat ( concat_embeddings , 1 ) return concat_embeddings