def METHOD_NAME(self, question):
    max_len = 12
    input_ids = torch.reshape(question['input_ids'], (-1, max_len))
    attention_mask = torch.reshape(question['attention_mask'], (-1, max_len))
    pooled_output = self.bert(input_ids=input_ids.to(self.device),
                              attention_mask=attention_mask.to(self.device),
                              return_dict=False)[1].to(self.device)
    question_vector = self.bert_reduction_layer(pooled_output).to(self.device)
    # question_vector = self.relu(question_vector).to(self.device)
    # question_vector = self.bert_reduction_layer_2(question_vector).to(self.device)
    # question_vector = self.relu(question_vector).to(self.device)
    # question_vector = self.bert_reduction_layer_3(question_vector).to(self.device)
    # question_vector = self.relu(question_vector).to(self.device)
    # question_vector = self.bert_reduction_layer_4(question_vector).to(self.device)
    # question_vector = self.relu(question_vector).to(self.device)
    # question_vector = self.bert_reduction_layer_5(question_vector).to(self.device)
    # question_vector = self.relu(question_vector).to(self.device)
    # question_vector = self.bert_reduction_layer_6(question_vector).to(self.device)
    return question_vector