code: def linear_data_sample_big_errs(N=40, rseed=0, m=3, b=-2):
      rng = np.random.RandomState(rseed)
  
      x = 10 * rng.rand(N)
      dy = m / 2 * (1 + rng.rand(N))
      dy[20:25] *= 10
      y = m * x + b + dy * rng.randn(N)
  
      return (x, y, dy)
  
imports: import numpy
label_desc: Use image node_id 2 for calling a global function with example usage: linear_data_sample_big_errs(N, rseed, m, b) and returns: x,  y,  dy