code: def cum_returns(returns, starting_value=0):
      """
      Compute cumulative returns from simple returns.
  
      Parameters
      ----------
      returns : pd.Series
          Daily returns of the strategy, noncumulative.
           - See full explanation in tears.create_full_tear_sheet.
      starting_value : float, optional
         The starting returns (default 1).
  
      Returns
      -------
      pandas.Series
          Series of cumulative returns.
  
      Notes
      -----
      For increased numerical accuracy, convert input to log returns
      where it is possible to sum instead of multiplying.
      """
  
      return ep.cum_returns(returns, starting_value=starting_value)
  
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 17 for calling a global function with example usage: cum_returns(returns, starting_value) and returns: ep