class_docstr: The Session Recommender model used for recommendation.
  
  # Arguments
   item_ount: The number of distinct items. Positive integer.
   item_embed: The output size of embedding layer. Positive integer.
   rnn_hidden_layers: Units of hidden layers for the mlp model. Array of positive integers.
   session_length: The max number of items in the sequence of a session
   include_history: Whether to include purchase history. Boolean. Default is true.
   mlp_hidden_layers: Units of hidden layers for the mlp model. Array of positive integers.
   history_length: The max number of items in the sequence of historical purchase
   
code: def __init__(
      self,
      item_count,
      item_embed,
      rnn_hidden_layers=[40, 20],
      session_length=0,
      include_history=False,
      mlp_hidden_layers=[40, 20],
      history_length=0,
      bigdl_type="float",
  ):
      assert (
          session_length > 0
      ), "session_length should align with input features"
      if include_history:
          assert (
              history_length > 0
          ), "history_length should align with input features"
      self.item_count = int(item_count)
      self.item_embed = int(item_embed)
      self.mlp_hidden_layers = [int(unit) for unit in mlp_hidden_layers]
      self.rnn_hidden_layers = [int(unit) for unit in rnn_hidden_layers]
      self.include_history = include_history
      self.session_length = int(session_length)
      self.history_length = int(history_length)
      self.bigdl_type = bigdl_type
      self.model = self.build_model()
      super(SessionRecommender, self).__init__(
          None,
          self.bigdl_type,
          self.item_count,
          self.item_embed,
          self.rnn_hidden_layers,
          self.session_length,
          self.include_history,
          self.mlp_hidden_layers,
          self.history_length,
          self.model,
      )
  
file_path: analytics-zoo/pyzoo/zoo/models/recommendation/session_recommender.py
imports: import sys
  from zoo.models.common import KerasZooModel
  from zoo.models.recommendation import Recommender
  from zoo.pipeline.api.keras.layers import *
  from zoo.pipeline.api.keras.models import *
  from zoo.common.utils import callZooFunc
label_desc: Use image node_id 1 to create a new SessionRecommender object from inherited base classes: Recommender with example: obj = SessionRecommender(item_count, item_embed, rnn_hidden_layers, session_length, include_history, mlp_hidden_layers, history_length, bigdl_type)