def load_data_wrapper():
 (tr_d, va_d, te_d) = load_data()
 training_inputs = [np.reshape(x, (784, 1)) for x in tr_d[0]]
 training_results = [vectorized_result(y) for y in tr_d[1]]
 training_data = zip(training_inputs, training_results)
 validation_inputs = [np.reshape(x, (784, 1)) for x in va_d[0]]
 validation_data = zip(validation_inputs, va_d[1])
 test_inputs = [np.reshape(x, (784, 1)) for x in te_d[0]]
 test_data = zip(test_inputs, te_d[1])
 return (training_data, validation_data, test_data)

def load_data_wrapper ( ) : ( tr_d , va_d , te_d ) = load_data ( ) training_inputs = [ np . reshape ( x , ( 784 , 1 ) ) for x in tr_d [ 0 ] ] training_results = [ vectorized_result ( y ) for y in tr_d [ 1 ] ] training_data = zip ( training_inputs , training_results ) validation_inputs = [ np . reshape ( x , ( 784 , 1 ) ) for x in va_d [ 0 ] ] validation_data = zip ( validation_inputs , va_d [ 1 ] ) test_inputs = [ np . reshape ( x , ( 784 , 1 ) ) for x in te_d [ 0 ] ] test_data = zip ( test_inputs , te_d [ 1 ] ) return ( training_data , validation_data , test_data )
return a tuple containing .