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: Load an existing SessionRecommender model (with weights).
  
  # Arguments
  path: The path for the pre-defined model.
        Local file system, HDFS and Amazon S3 are supported.
        HDFS path should be like 'hdfs://[host]:[port]/xxx'.
        Amazon S3 path should be like 's3a://bucket/xxx'.
  weight_path: The path for pre-trained weights if any. Default is None.
docstr_tok: ["Load","an","existing","SessionRecommender","model","(","with","weights",")",".","#","Arguments","path",":","The","path","for","the","pre-defined","model",".","Local","file","system",",","HDFS","and","Amazon","S3","are","supported",".","HDFS","path","should","be","like","'hdfs",":","\/\/","[","host","]",":","[","port","]","\/xxx","'",".","Amazon","S3","path","should","be","like","'s3a",":","\/\/bucket\/xxx","'",".","weight_path",":","The","path","for","pre-trained","weights","if","any",".","Default","is","None","."]
code: def load_model(path, weight_path=None, bigdl_type="float"):
      """
      Load an existing SessionRecommender model (with weights).
  
      # Arguments
      path: The path for the pre-defined model.
            Local file system, HDFS and Amazon S3 are supported.
            HDFS path should be like 'hdfs://[host]:[port]/xxx'.
            Amazon S3 path should be like 's3a://bucket/xxx'.
      weight_path: The path for pre-trained weights if any. Default is None.
      """
      jmodel = callZooFunc(
          bigdl_type, "loadSessionRecommender", path, weight_path
      )
      model = KerasZooModel._do_load(jmodel, bigdl_type)
      model.__class__ = SessionRecommender
      return 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 7 for calling the SessionRecommender obj's underlying member method code with example usage: obj.load_model(path, weight_path, bigdl_type) and returns: model