code: def build_step_function_dataset(D=1, n_data=40, noise_std=0.1):
      rs = npr.RandomState(0)
      inputs = np.linspace(-2, 2, num=n_data)
      targets = np.sign(inputs) + rs.randn(n_data) * noise_std
      inputs = inputs.reshape((len(inputs), D))
      return inputs, targets
  
file_path: autograd/examples/deep_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 import value_and_grad
  from scipy.optimize import minimize
  from gaussian_process import make_gp_funs, rbf_covariance
label_desc: Use image node_id 1 for calling a global function with example usage: build_step_function_dataset(D, n_data, noise_std) and returns: inputs,  targets