code: def _mcse_mean(ary):
      """Compute the Markov Chain mean error."""
      _numba_flag = Numba.numba_flag
      ary = np.asarray(ary)
      if _not_valid(ary, shape_kwargs=dict(min_draws=4, min_chains=1)):
          return np.nan
      ess = _ess_mean(ary)
      if _numba_flag:
          sd = _sqrt(svar(np.ravel(ary), ddof=1), np.zeros(1))
      else:
          sd = np.std(ary, ddof=1)
      mcse_mean_value = sd / np.sqrt(ess)
      return mcse_mean_value
  
imports: import warnings
  from collections.abc import Sequence
  import numpy
  import packaging
  import pandas
  import scipy
  from scipy import stats
  from ..data import convert_to_dataset
  from ..utils import Numba, _numba_var, _stack, _var_names
  from .density_utils import histogram
  from .stats_utils import _circular_standard_deviation, _sqrt
  from .stats_utils import autocov
  from .stats_utils import not_valid
  from .stats_utils import quantile
  from .stats_utils import stats_variance_2d
  from .stats_utils import wrap_xarray_ufunc
label_desc: Use image node_id 29 for calling a global function with example usage: _mcse_mean(ary) and returns: mcse_mean_value, np