code: def alpha_beta(returns, factor_returns):
      """
      Calculates both alpha and beta.
  
      Parameters
      ----------
      returns : pd.Series
          Daily returns of the strategy, noncumulative.
          - See full explanation in :func:`~pyfolio.timeseries.cum_returns`.
      factor_returns : pd.Series
          Daily noncumulative returns of the benchmark factor to which betas are
          computed. Usually a benchmark such as market returns.
           - This is in the same style as returns.
  
      Returns
      -------
      float
          Alpha.
      float
          Beta.
      """
  
      return ep.alpha_beta(returns, factor_returns=factor_returns)
  
imports: from __future__ import division
  from collections import OrderedDict
  from functools import partial
  import empyrical
  import numpy
  import pandas
  import scipy
  import scipy.stats
  from sklearn import linear_model
  from .deprecate import deprecated
  from .interesting_periods import PERIODS
  from .txn import get_turnover
  from .utils import APPROX_BDAYS_PER_MONTH, APPROX_BDAYS_PER_YEAR
  from .utils import DAILY
label_desc: Use image node_id 10 for calling a global function with example usage: alpha_beta(returns, factor_returns) and returns: ep