code: def hyperboloid_grad(x, y):
      s = np.sqrt(1 + np.sum(x**2))
      t = np.sqrt(1 + np.sum(y**2))
  
      B = s * t
      for i in range(x.shape[0]):
          B -= x[i] * y[i]
  
      if B <= 1:
          B = 1.0 + 1e-8
  
      grad_coeff = 1.0 / (np.sqrt(B - 1) * np.sqrt(B + 1))
  
      # return np.arccosh(B), np.zeros(x.shape[0])
  
      grad = np.zeros(x.shape[0])
      for i in range(x.shape[0]):
          grad[i] = grad_coeff * (((x[i] * t) / s) - y[i])
  
      return np.arccosh(B), grad
  
imports: import numba
  import numpy
  import scipy.stats
  from sklearn.metrics import pairwise_distances
label_desc: Use image node_id 13 for calling a global function with example usage: hyperboloid_grad(x, y) and returns: np,  grad