code: def build_toy_dataset(D=1, n_data=20, noise_std=0.1):
      rs = npr.RandomState(0)
      inputs = np.concatenate(
          [
              np.linspace(0, 3, num=n_data / 2),
              np.linspace(6, 8, num=n_data / 2),
          ]
      )
      targets = (np.cos(inputs) + rs.randn(n_data) * noise_std) / 2.0
      inputs = (inputs - 4.0) / 2.0
      inputs = inputs.reshape((len(inputs), D))
      return inputs, targets
  
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 3 for calling a global function with example usage: build_toy_dataset(D, n_data, noise_std) and returns: inputs,  targets