code: def priors_visualise(self, n_draws=10):
      # 1-D only, plot priors
      # we currently have data noise included in this, could remove it to get smooth
  
      print_w_time("getting priors")
      # get some priors
      y_samples_prior = np.random.multivariate_normal(
          np.zeros(self.x_predict.shape[0]), self.cov_xx, n_draws
      ).T  # mean, covariance, size
  
      # plot priors
      fig = plt.figure()
      plt.plot(
          self.x_predict, y_samples_prior, "k", lw=0.5, label="Priors"
      )
      plt.xlabel("$x$")
      plt.ylabel("$f(x)$")
      title = (
          self.kernel_type
          + ", priors, b_0: "
          + str(self.b_0_var)
          + ", w_0: "
          + str(self.w_0_var)
      )
      plt.title(title)
      # plt.xlim(-6, 6)
      fig.show()
  
      return
  
file_path: elu-gelu-kernels/code/experiments/02_shallow_experiments/module_gp_gelu.py
imports: import numpy
  from .utils import *
  from ...kernels.nn_kernel_relu import NNKernelRelu
  from ...kernels.nn_kernel_gelu import NNKernelGelu
  from ...kernels.nn_kernel_lrelu import NNKernelLRelu
  from ...kernels.nn_kernel_elu import NNKernelElu
  from ...kernels.nn_kernel_erf import NNKernelErf
label_desc: Use image node_id 4 for calling the gp_model obj's underlying member method code with example usage: obj.priors_visualise(n_draws) without return types