def olympic_sprints(data_set='<STR_LIT>'):
X = np.zeros((<NUM_LIT:0>, <NUM_LIT:2>))<EOL>Y = np.zeros((<NUM_LIT:0>, <NUM_LIT:1>))<EOL>for i, dataset in enumerate([olympic_100m_men,<EOL>olympic_100m_women,<EOL>olympic_200m_men,<EOL>olympic_200m_women,<EOL>olympic_400m_men,<EOL>olympic_400m_women]):<EOL><INDENT>data = dataset()<EOL>year = data['<STR_LIT:X>']<EOL>time = data['<STR_LIT:Y>']<EOL>X = np.vstack((X, np.hstack((year, np.ones_like(year)*i))))<EOL>Y = np.vstack((Y, time))<EOL><DEDENT>data['<STR_LIT:X>'] = X<EOL>data['<STR_LIT:Y>'] = Y<EOL>data['<STR_LIT:info>'] = "<STR_LIT>"<EOL>return data_details_return({<EOL>'<STR_LIT:X>': X,<EOL>'<STR_LIT:Y>': Y,<EOL>'<STR_LIT:info>': "<STR_LIT>",<EOL>'<STR_LIT>': {<EOL><NUM_LIT:0>:'<STR_LIT>',<EOL><NUM_LIT:1>:'<STR_LIT>',<EOL><NUM_LIT:2>:'<STR_LIT>',<EOL><NUM_LIT:3>:'<STR_LIT>',<EOL><NUM_LIT:4>:'<STR_LIT>',<EOL><NUM_LIT:5>:'<STR_LIT>'}<EOL>}, data_set)<EOL>
All olympics sprint winning times for multiple output prediction.