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
      -------
      pd.Series or np.ndarray
      """
      if (
          isinstance(adjustment_factor, (float, int))
          and adjustment_factor == 0
      ):
          return returns
      return returns - adjustment_factor
  
imports: from __future__ import division
  from functools import wraps
  import pandas
  import numpy
  from scipy import stats
  from six import iteritems
  from empyrical.utils import nanmean, nanstd, nanmin
label_desc: Use image node_id 1 for calling a global function with example usage: _adjust_returns(returns, adjustment_factor) and returns: unknown, returns