class_docstr: A2SVD Model (Attentive Asynchronous Singular Value Decomposition)
  
  It extends ASVD with an attention module.
  
  :Citation:
  
      ASVD: Y. Koren, "Factorization Meets the Neighborhood: a Multifaceted Collaborative
      Filtering Model", in Proceedings of the 14th ACM SIGKDD international conference on
      Knowledge discovery and data mining, pages 426–434, ACM, 2008.
  
      A2SVD: Z. Yu, J. Lian, A. Mahmoody, G. Liu and X. Xie, "Adaptive User Modeling with
      Long and Short-Term Preferences for Personailzed Recommendation", in Proceedings of
      the 28th International Joint Conferences on Artificial Intelligence, IJCAI’19,
      Pages 4213-4219, AAAI Press, 2019.
class_docstr_tok: ["A2SVD","Model","(","Attentive","Asynchronous","Singular","Value","Decomposition",")","It","extends","ASVD","with","an","attention","module",".",":","Citation",":","ASVD",":","Y.","Koren",",","``","Factorization","Meets","the","Neighborhood",":","a","Multifaceted","Collaborative","Filtering","Model","''",",","in","Proceedings","of","the","14th","ACM","SIGKDD","international","conference","on","Knowledge","discovery","and","data","mining",",","pages","426\u2013434",",","ACM",",","2008",".","A2SVD",":","Z.","Yu",",","J.","Lian",",","A.","Mahmoody",",","G.","Liu","and","X.","Xie",",","``","Adaptive","User","Modeling","with","Long","and","Short-Term","Preferences","for","Personailzed","Recommendation","''",",","in","Proceedings","of","the","28th","International","Joint","Conferences","on","Artificial","Intelligence",",","IJCAI","\u2019","19",",","Pages","4213-4219",",","AAAI","Press",",","2019","."]
docstr: The main function to create A2SVD model.
  
  Returns:
      object: The output of A2SVD section.
docstr_tok: ["The","main","function","to","create","A2SVD","model",".","Returns",":","object",":","The","output","of","A2SVD","section","."]
code: def _build_seq_graph(self):
      """The main function to create A2SVD model.
  
      Returns:
          object: The output of A2SVD section.
      """
      hparams = self.hparams
      with tf.compat.v1.variable_scope("a2svd"):
          hist_input = tf.concat(
              [
                  self.item_history_embedding,
                  self.cate_history_embedding,
              ],
              2,
          )
          with tf.compat.v1.variable_scope("Attention_layer"):
              att_outputs1 = self._attention(
                  hist_input, hparams.attention_size
              )
              asvd_output = tf.reduce_sum(
                  input_tensor=att_outputs1, axis=1
              )
              tf.compat.v1.summary.histogram(
                  "a2svd_output", asvd_output
              )
          model_output = tf.concat(
              [asvd_output, self.target_item_embedding], 1
          )
          self.model_output = model_output
          tf.compat.v1.summary.histogram("model_output", model_output)
          return model_output
  
imports: import tensorflow
  from recommenders.models.deeprec.models.sequential.sequential_base_model import SequentialBaseModel
label_desc: Use image node_id 1 for calling the A2SVDModel obj's underlying member method code with example usage: obj._build_seq_graph() and returns: model_output