code: def build_toy_dataset(N, noise_std=0.5):
      X = np.concatenate(
          [np.linspace(0, 2, num=N / 2), np.linspace(6, 8, num=N / 2)]
      )
      y = 2.0 * X + 10 * np.random.normal(0, noise_std, size=N)
      X = X.reshape((N, 1))
      return X, y
  
file_path: edward/examples/bayesian_linear_regression.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import edward
  import matplotlib.pyplot
  import seaborn
  import numpy
  import tensorflow
  from edward.models import Normal, Empirical
label_desc: Use image node_id 1 for calling a global function with example usage: build_toy_dataset(N, noise_std) and returns: X,  y