original_docstring: 
          Calls training depending on the MK model
          
docstring: Calls training depending on the MK model
docstring_tokens: Calls training depending on the MK model
code: def call_training(self, val_split: float = 0.1, epoch_size: int = 2, batch_size: int = 128, verbosity_mode: int = 1,
                        callback_list: list = []) -> list:
          '''
          Calls training depending on the MK model
          '''
          x_data = self.training_data[0]
          [h, alpha, u] = self.model(x_data[:1000])
  
          # y_data = [h,alpha,u, alpha (for KLDivergence)]
          y_data = [self.training_data[2], self.training_data[1], self.training_data[0]]  # , self.trainingData[1]]
          self.history = self.model.fit(x=x_data, y=y_data,
                                        validation_split=val_split, epochs=epoch_size,
                                        batch_size=batch_size, verbose=verbosity_mode,
                                        callbacks=callback_list, shuffle=True)
  
          [h, alpha, u] = self.model(x_data)
  
          return self.history
code_tokens: def call_training ( self , val_split : float = 0.1 , epoch_size : int = 2 , batch_size : int = 128 , verbosity_mode : int = 1 , callback_list : list = [ ] ) -> list : x_data = self . training_data [ 0 ] [ h , alpha , u ] = self . model ( x_data [ : 1000 ] ) y_data = [ self . training_data [ 2 ] , self . training_data [ 1 ] , self . training_data [ 0 ] ] self . history = self . model . fit ( x = x_data , y = y_data , validation_split = val_split , epochs = epoch_size , batch_size = batch_size , verbose = verbosity_mode , callbacks = callback_list , shuffle = True ) [ h , alpha , u ] = self . model ( x_data ) return self . history
short_docstring: Calls training depending on the MK model
short_docstring_tokens: Calls training depending on the MK model
comment: '''
          Calls training depending on the MK model
          '''$SEP$# y_data = [h,alpha,u, alpha (for KLDivergence)]$SEP$# , self.trainingData[1]]