code: def school_model():
      """
      Schools model to use in testing model_to_networkx function
      """
      J = 8
      y = np.array([28, 8, -3, 7, -1, 1, 18, 12])
      sigma = np.array([15, 10, 16, 11, 9, 11, 10, 18])
      with pm.Model() as schools:
          eta = pm.Normal("eta", 0, 1, shape=J)
          mu = pm.Normal("mu", 0, sigma=1e6)
          tau = pm.HalfCauchy("tau", 25)
          theta = mu + tau * eta
          obs = pm.Normal("obs", theta, sigma=sigma, observed=y)
      return schools
  
imports: import warnings
  import numpy
  import pytensor
  import pytensor.tensor
  import pytest
  from pytensor.compile.sharedvalue import SharedVariable
  from pytensor.tensor.variable import TensorConstant
  import pymc
  from pymc.exceptions import ImputationWarning
  from pymc.model_graph import ModelGraph, model_to_graphviz, model_to_networkx
label_desc: Use image node_id 1 for calling a global function with example usage: school_model() and returns: schools