code: def _adjust_returns(returns, adjustment_factor):
      """
      Returns the returns series adjusted by adjustment_factor. Optimizes for the
      case of adjustment_factor being 0 by returning returns itself, not a copy!
  
      Parameters
      ----------
      returns : pd.Series or np.ndarray
      adjustment_factor : pd.Series or np.ndarray or float or int
  
      Returns
      -------
      adjusted_returns : array-like
      """
      if (
          isinstance(adjustment_factor, (float, int))
          and adjustment_factor == 0
      ):
          return returns
      return returns - adjustment_factor
  
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 4 for calling a global function with example usage: _adjust_returns(returns, adjustment_factor) and returns: unknown, returns