class_docstr: The same data information shared by the Tower model and its feature generation part.
  
  Each instance could contain the following fields:
  indicator_cols: Data of indicator_cols will be fed into model as one-hot vectors.
                  List of String. Default is an empty list.
  indicator_dims: Dimensions of indicator_cols. The dimensions of the data in
                  indicator_cols should be within the range of indicator_dims.
                  List of int. Default is an empty list.
  embed_cols: Data of embed_cols will be fed into the  model as embeddings.
              List of String. Default is an empty list.
  embed_in_dims: Input dimension of the data in embed_cols. The dimensions of the data in
                 embed_cols should be within the range of embed_in_dims.
                 List of int. Default is an empty list.
  embed_out_dims: The out dimensions of embeddings. List of int. Default is an empty list.
  numerical_cols: Data of numerical_cols will be treated as continuous values
                  List of String. Default is an empty list.
  numerical_dims: dimention of numerical_cols.
  name: string
class_docstr_tok: ["The","same","data","information","shared","by","the","Tower","model","and","its","feature","generation","part",".","Each","instance","could","contain","the","following","fields",":","indicator_cols",":","Data","of","indicator_cols","will","be","fed","into","model","as","one-hot","vectors",".","List","of","String",".","Default","is","an","empty","list",".","indicator_dims",":","Dimensions","of","indicator_cols",".","The","dimensions","of","the","data","in","indicator_cols","should","be","within","the","range","of","indicator_dims",".","List","of","int",".","Default","is","an","empty","list",".","embed_cols",":","Data","of","embed_cols","will","be","fed","into","the","model","as","embeddings",".","List","of","String",".","Default","is","an","empty","list",".","embed_in_dims",":","Input","dimension","of","the","data","in","embed_cols",".","The","dimensions","of","the","data","in","embed_cols","should","be","within","the","range","of","embed_in_dims",".","List","of","int",".","Default","is","an","empty","list",".","embed_out_dims",":","The","out","dimensions","of","embeddings",".","List","of","int",".","Default","is","an","empty","list",".","numerical_cols",":","Data","of","numerical_cols","will","be","treated","as","continuous","values","List","of","String",".","Default","is","an","empty","list",".","numerical_dims",":","dimention","of","numerical_cols",".","name",":","string"]
code: def __reduce__(self):
      return ColumnInfoTower, (
          self.indicator_cols,
          self.indicator_dims,
          self.embed_cols,
          self.embed_in_dims,
          self.embed_out_dims,
          self.numerical_cols,
          self.numerical_dims,
          self.name,
      )
  
file_path: BigDL/python/friesian/example/two_tower/model.py
imports: from bigdl.dllib.keras.layers import *
  from tensorflow.keras.layers import Input, Embedding, Dense, Flatten, concatenate, Lambda
  import tensorflow
label_desc: Use image node_id 2 for calling the ColumnInfoTower obj's underlying member method code with example usage: obj.__reduce__() and returns: ColumnInfoTower,  self,  self,  self,  self,  self,  self,  self,  self