class_docstr: Caser Model
  
  :Citation:
  
      J. Tang and K. Wang, "Personalized top-n sequential recommendation via convolutional
      sequence embedding", in Proceedings of the Eleventh ACM International Conference on
      Web Search and Data Mining, ACM, 2018.
docstr: Call a CNN layer.
  
  Returns:
      object: The output of cnn section.
docstr_tok: ["Call","a","CNN","layer",".","Returns",":","object",":","The","output","of","cnn","section","."]
code: def _build_cnn(self, history_matrix, nums, shape):
      """Call a CNN layer.
  
      Returns:
          object: The output of cnn section.
      """
      return tf.compat.v1.layers.conv1d(
          history_matrix,
          nums,
          shape,
          activation=tf.nn.relu,
          name="conv_" + str(shape),
      )
  
file_path: recommenders/recommenders/models/deeprec/models/sequential/caser.py
imports: import tensorflow
  from recommenders.models.deeprec.models.sequential.sequential_base_model import SequentialBaseModel
label_desc: Use image node_id 5 for calling the CaserModel obj's underlying member method code with example usage: obj._build_cnn(history_matrix, nums, shape) and returns: tf