code: def plot_linear_regression():
      a = 0.5
      b = 1.0
  
      # x from 0 to 10
      x = 30 * np.random.random(20)
  
      # y = a*x + b with noise
      y = a * x + b + np.random.normal(size=x.shape)
  
      # create a linear regression classifier
      clf = LinearRegression()
      clf.fit(x[:, None], y)
  
      # predict y from the data
      x_new = np.linspace(0, 30, 100)
      y_new = clf.predict(x_new[:, None])
  
      # plot the results
      ax = plt.axes()
      ax.scatter(x, y)
      ax.plot(x_new, y_new)
  
      ax.set_xlabel("x")
      ax.set_ylabel("y")
  
      ax.axis("tight")
  
label_desc: Use image node_id 1 for calling a global function with example usage: plot_linear_regression() without return types