code: def simulate_paths(
      is_returns,
      num_days,
      starting_value=1,
      num_samples=1000,
      random_seed=None,
  ):
      """
      Gnerate alternate paths using available values from in-sample returns.
  
      Parameters
      ----------
      is_returns : pandas.core.frame.DataFrame
          Non-cumulative in-sample returns.
      num_days : int
          Number of days to project the probability cone forward.
      starting_value : int or float
          Starting value of the out of sample period.
      num_samples : int
          Number of samples to draw from the in-sample daily returns.
          Each sample will be an array with length num_days.
          A higher number of samples will generate a more accurate
          bootstrap cone.
      random_seed : int
          Seed for the pseudorandom number generator used by the pandas
          sample method.
  
      Returns
      -------
      samples : numpy.ndarray
      """
  
      samples = np.empty((num_samples, num_days))
      seed = np.random.RandomState(seed=random_seed)
      for i in range(num_samples):
          samples[i, :] = is_returns.sample(
              num_days, replace=True, random_state=seed
          )
  
      return samples
  
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 33 for calling a global function with example usage: simulate_paths(is_returns, num_days, starting_value, num_samples, random_seed) and returns: samples