parameters: (self, table_id, learning_rate, param_vars, grad_vars)
docstring: r"""
          Args:
              table_id(int): id of sparse params table
              learning_rate(float): the learning rate used to update parameters. \
                  Can be a float value
              param_var(list): all dense param. it is a list.
              grad_var(list): all dense grad parm it is a list.
          Returns:
              return None
docstring_summary: r"""
          Args:
              table_id(int): id of sparse params table
              learning_rate(float): the learning rate used to update parameters. \
                  Can be a float value
              param_var(list): all dense param. it is a list.
              grad_var(list): all dense grad parm it is a list.
          Returns:
              return None
docstring_tokens: r Args : table_id ( int ) : id of sparse params table learning_rate ( float ) : the learning rate used to update parameters . \ Can be a float value param_var ( list ) : all dense param . it is a list . grad_var ( list ) : all dense grad parm it is a list . Returns : return None
function: def add_dense_table(self, table_id, learning_rate, param_vars, grad_vars):
          r"""
          Args:
              table_id(int): id of sparse params table
              learning_rate(float): the learning rate used to update parameters. \
                  Can be a float value
              param_var(list): all dense param. it is a list.
              grad_var(list): all dense grad parm it is a list.
          Returns:
              return None 
          """
          table = self.worker_.dense_table.add()
          table.table_id = table_id
          table.dense_variable_name.extend(
              filter(lambda x: x.find("embedding") == -1,
                     [p.name for p in param_vars]))
          table.dense_gradient_variable_name.extend(
              filter(lambda x: x.find("embedding") == -1,
                     [g.name for g in grad_vars]))
function_tokens: def add_dense_table ( self , table_id , learning_rate , param_vars , grad_vars ) : table = self . worker_ . dense_table . add ( ) table . table_id = table_id table . dense_variable_name . extend ( filter ( lambda x : x . find ( "embedding" ) == - 1 , [ p . name for p in param_vars ] ) ) table . dense_gradient_variable_name . extend ( filter ( lambda x : x . find ( "embedding" ) == - 1 , [ g . name for g in grad_vars ] ) )