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
   
docstr: recommend for sessions given rdd of samples or list of samples.
  
  # Arguments
  sessions: rdd of samples or list of samples.
  max_items:   Number of items to be recommended to each user. Positive integer.
  zero_based_label: True if data starts from 0, False if data starts from 1
  :return rdd of list of list(item, probability),
docstr_tok: ["recommend","for","sessions","given","rdd","of","samples","or","list","of","samples",".","#","Arguments","sessions",":","rdd","of","samples","or","list","of","samples",".","max_items",":","Number","of","items","to","be","recommended","to","each","user",".","Positive","integer",".","zero_based_label",":","True","if","data","starts","from","0",",","False","if","data","starts","from","1",":","return","rdd","of","list","of","list","(","item",",","probability",")",","]
code: def recommend_for_session(
      self, sessions, max_items, zero_based_label
  ):
      """
      recommend for sessions given rdd of samples or list of samples.
  
      # Arguments
      sessions: rdd of samples or list of samples.
      max_items:   Number of items to be recommended to each user. Positive integer.
      zero_based_label: True if data starts from 0, False if data starts from 1
      :return rdd of list of list(item, probability),
      """
      if isinstance(sessions, list):
          sc = get_spark_context()
          sessions_rdd = sc.parallelize(sessions)
      elif isinstance(sessions, RDD):
          sessions_rdd = sessions
      else:
          raise TypeError(
              "Unsupported training data type: %s" % type(sessions)
          )
      results = callZooFunc(
          self.bigdl_type,
          "recommendForSession",
          self.value,
          sessions_rdd,
          max_items,
          zero_based_label,
      )
  
      if isinstance(sessions, list):
          return results.collect()
      else:
          return results
  
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 6 for calling the SessionRecommender obj's underlying member method code with example usage: obj.recommend_for_session(sessions, max_items, zero_based_label) and returns: results, results