code: def _slice_sampling(
      ptarget,
      n_samples,
      x0,
      n_burnin=20,
      thin=10,
      random_state=None,
      n_jobs=1,
  ):
      """Slice sampling procedure.
  
      Implementation of a slice sampling algorithm for sampling from any target
      pdf or a multiple of it. The implementation follows the description given
      in page 375 of David McKay's book "Information Theory, Inference, and
      Learning Algorithms" (2003).
  
      Parameters
      ----------
      ptarget : function with one input
          The target pdf to sample from or a multiple of it.
      n_samples : int
          How many samples to get from the ptarget distribution.
      x0 : array
          Initial state for the MCMC procedure. Note that the shape of this array
          defines the dimensionality n_dim of the data points to be sampled.
      n_burnin : int, default=20
          How many samples to discard from the beginning of the chain generated
          by the slice sampling procedure. Usually the first samples are prone to
          non-stationary behavior and do not follow very well the target pdf.
      thin : int, default=10
          Thinning factor for the slice sampling procedure. MCMC samples are
          often correlated between them, so taking one sample every `thin`
          samples can help reducing this correlation. Note that this makes the
          algorithm actually sample `thin x n_samples` samples from the pdf, so
          expect the whole sampling procedure to take longer.
      random_state : int, RandomState instance or None, default=None
          Pass an int for reproducible output across multiple function calls.
      n_jobs : int, default=1
          The number of jobs to use for the computation. This works by computing
          each of the class centroid in parallel. If -1 all CPUs are used.
  
      Returns
      -------
      samples : ndarray, shape (n_samples, n_dim)
          Samples from the target pdf.
      """
  
      if (n_samples <= 0) or (not isinstance(n_samples, int)):
          raise ValueError(
              f"n_samples must be a positive integer (Got {n_samples})"
          )
      if (n_burnin <= 0) or (not isinstance(n_burnin, int)):
          raise ValueError(
              f"n_samples must be a positive integer (Got {n_burnin})"
          )
      if (thin <= 0) or (not isinstance(thin, int)):
          raise ValueError(
              f"thin must be a positive integer (Got {thin})"
          )
  
      rs = check_random_state(random_state)
      w = 1.0  # initial bracket width
  
      n_samples_total = (n_samples + n_burnin) * thin
  
      samples = Parallel(n_jobs=n_jobs)(
          delayed(_slice_one_sample)(ptarget, x0, w, rs)
          for _ in range(n_samples_total)
      )
  
      samples = np.array(samples)[(n_burnin * thin) :][::thin]
  
      return samples
  
file_path: pyRiemann/pyriemann/datasets/sampling.py
imports: from functools import partial
  import warnings
  import numpy
  from scipy.stats import multivariate_normal
  from sklearn.utils import check_random_state
  from joblib import Parallel, delayed
  from ..utils.base import sqrtm, expm
  from ..utils.test import is_sym_pos_semi_def
label_desc: Use image node_id 6 for calling a global function with example usage: _slice_sampling(ptarget, n_samples, x0, n_burnin, thin, random_state, n_jobs) and returns: samples