code: def rbf_covariance(kernel_params, x, xp):
      output_scale = np.exp(kernel_params[0])
      lengthscales = np.exp(kernel_params[1:])
      diffs = np.expand_dims(x / lengthscales, 1) - np.expand_dims(
          xp / lengthscales, 0
      )
      return output_scale * np.exp(-0.5 * np.sum(diffs**2, axis=2))
  
file_path: autograd/examples/gaussian_process.py
imports: from __future__ import absolute_import
  from __future__ import print_function
  import matplotlib.pyplot
  import autograd.numpy
  import autograd.numpy.random
  from autograd.numpy.linalg import solve
  import autograd.scipy.stats.multivariate_normal
  from autograd import value_and_grad
  from scipy.optimize import minimize
label_desc: Use image node_id 2 for calling a global function with example usage: rbf_covariance(kernel_params, x, xp) and returns: unknown