code: def _rhat(ary):
      """Compute the rhat for a 2d array."""
      _numba_flag = Numba.numba_flag
      ary = np.asarray(ary, dtype=float)
      if _not_valid(ary, check_shape=False):
          return np.nan
      _, num_samples = ary.shape
  
      # Calculate chain mean
      chain_mean = np.mean(ary, axis=1)
      # Calculate chain variance
      chain_var = _numba_var(svar, np.var, ary, axis=1, ddof=1)
      # Calculate between-chain variance
      between_chain_variance = num_samples * _numba_var(
          svar, np.var, chain_mean, axis=None, ddof=1
      )
      # Calculate within-chain variance
      within_chain_variance = np.mean(chain_var)
      # Estimate of marginal posterior variance
      rhat_value = np.sqrt(
          (
              between_chain_variance / within_chain_variance
              + num_samples
              - 1
          )
          / (num_samples)
      )
      return rhat_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 11 for calling a global function with example usage: _rhat(ary) and returns: rhat_value, np