code: def _rejection_sampling_2D_gfunction_minus(sigma, r_sample):
      """Auxiliary function for the 2D rejection sampling algorithm.
  
      It is used in the case where r is sampled with the function g-.
  
      Parameters
      ----------
      sigma : float
          Dispersion of the Riemannian Gaussian distribution.
      r_samples : ndarray, shape (1, n_dim)
          Sample of the r parameters of the Riemannian Gaussian distribution.
  
      Returns
      -------
      p : float
          Probability of acceptation.
  
      Notes
      -----
      .. versionadded:: 0.4
  
      """
      mu_b = np.array([(sigma**2) / 2, -(sigma**2) / 2])
      cov_matrix = (sigma**2) * np.eye(2)
      m = np.pi * (sigma**2) * np.exp(sigma**2 / 4)
      if r_sample[0] < r_sample[1]:
          num = _pdf_r(r_sample, sigma)
          den = (
              multivariate_normal.pdf(
                  r_sample, mean=mu_b, cov=cov_matrix
              )
              * m
          )
          return num / den
      return 0
  
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 3 for calling a global function with example usage: _rejection_sampling_2D_gfunction_minus(sigma, r_sample) and returns: int, unknown