code: def roll_alpha_beta(returns, factor_returns, window=10, **kwargs):
      """
      Computes alpha and beta over a rolling window.
  
      Parameters
      ----------
      lhs : array-like
          The first array to pass to the rolling alpha-beta.
      rhs : array-like
          The second array to pass to the rolling alpha-beta.
      window : int
          Size of the rolling window in terms of the periodicity of the data.
      out : array-like, optional
          Array to use as output buffer.
          If not passed, a new array will be created.
      **kwargs
          Forwarded to :func:`~empyrical.alpha_beta`.
      """
      returns, factor_returns = _aligned_series(returns, factor_returns)
  
      return roll_alpha_beta_aligned(
          returns, factor_returns, window=window, **kwargs
      )
  
imports: from __future__ import division
  import math
  import pandas
  import numpy
  from math import pow
  from scipy import stats, optimize
  from six import iteritems
  from sys import float_info
  from .utils import nanmean, nanstd, nanmin, up, down, roll, rolling_window
  from .periods import ANNUALIZATION_FACTORS, APPROX_BDAYS_PER_YEAR
  from .periods import DAILY, WEEKLY, MONTHLY, QUARTERLY, YEARLY
label_desc: Use image node_id 23 for calling a global function with example usage: roll_alpha_beta(returns, factor_returns, window) and returns: roll_alpha_beta_aligned