code: def get_states_from_R(results_R, k_states):
      if k_states > 1:
          xhat_R = results_R["states"][1:, 0:k_states]
      else:
          xhat_R = results_R["states"][1:]
          xhat_R = np.reshape(xhat_R, (len(xhat_R), 1))
      return xhat_R
  
imports: from statsmodels.compat.pandas import QUARTER_END
  from statsmodels.compat.platform import PLATFORM_LINUX32, PLATFORM_WIN
  from itertools import product
  import json
  import pathlib
  import numpy
  from numpy.testing import assert_allclose, assert_almost_equal
  import pandas
  import pytest
  import scipy.stats
  from statsmodels.tsa.exponential_smoothing.ets import ETSModel
  import statsmodels.tsa.holtwinters
  import statsmodels.tsa.statespace.exponential_smoothing
label_desc: Use image node_id 14 for calling a global function with example usage: get_states_from_R(results_R, k_states) and returns: xhat_R