class_docstr: The Wide and Deep model used for recommendation.
  
  # Arguments
  class_num: The number of classes. Positive int.
  column_info: An instance of ColumnFeatureInfo.
  model_type: String. 'wide', 'deep' and 'wide_n_deep' are supported. Default is 'wide_n_deep'.
  hidden_layers: Units of hidden layers for the deep model.
                 Tuple of positive int. Default is (40, 20, 10).
class_docstr_tok: ["The","Wide","and","Deep","model","used","for","recommendation",".","#","Arguments","class_num",":","The","number","of","classes",".","Positive","int",".","column_info",":","An","instance","of","ColumnFeatureInfo",".","model_type",":","String",".","'wide","'",",","'deep","'","and","'wide_n_deep","'","are","supported",".","Default","is","'wide_n_deep","'",".","hidden_layers",":","Units","of","hidden","layers","for","the","deep","model",".","Tuple","of","positive","int",".","Default","is","(","40",",","20",",","10",")","."]
code: def _deep_hidden(self, merge_list):
      if len(merge_list) == 1:
          merged = merge_list[0]
      else:
          merged = merge(merge_list, "concat")
      linear = Dense(self.hidden_layers[0], activation="relu")(merged)
  
      for ilayer in range(1, len(self.hidden_layers)):
          linear_mid = Dense(
              self.hidden_layers[ilayer], activation="relu"
          )(linear)
          linear = linear_mid
      last = Dense(self.class_num, activation="relu")(linear)
      return last
  
file_path: analytics-zoo/pyzoo/zoo/models/recommendation/wide_and_deep.py
imports: import sys
  from zoo.models.common import *
  from zoo.models.recommendation import Recommender
  from zoo.common.utils import callZooFunc
  from zoo.pipeline.api.keras.layers import *
label_desc: Use image node_id 3 for calling the WideAndDeep obj's underlying member method code with example usage: obj._deep_hidden(merge_list) and returns: last