code: def create_dataset():
      np.random.seed(43)
      n = 150
      w0 = 0.125
      b0 = 5.0
      x_range = [-20, 60]
      x_tst = np.linspace(*x_range).astype(np.float32)
  
      def s(x):
          g = (x - x_range[0]) / (x_range[1] - x_range[0])
          return 3 * (0.25 + g**2.0)
  
      x = (x_range[1] - x_range[0]) * np.random.rand(n) + x_range[0]
      eps = np.random.randn(n) * s(x)
      y = (w0 * x * (1.0 + np.sin(x)) + b0) + eps
      x = x[..., np.newaxis]
      x_tst = x_tst[..., np.newaxis]
  
      return y, x, x_tst
  
file_path: probability/tensorflow_probability/python/layers/dense_variational_v2_test.py
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.internal import tf_keras
  from tensorflow_probability.python.layers import dense_variational_v2
  from tensorflow_probability.python.layers import distribution_layer
  from tensorflow_probability.python.layers import variable_input
label_desc: Use image node_id 1 for calling a global function with example usage: create_dataset() and returns: y,  x,  x_tst, unknown