old_contents: 
  import numpy as np
  from tensorprob import Model, Normal, Parameter, fisher
  
  def test_fisher():
      with Model() as model:
  
          mu = Parameter()
          sigma = Parameter(lower=0)
          X = Normal(mu, sigma)
  
      model.observed(X)
      model.initialize({
          mu: 2,
          sigma: 2,
      })
      np.random.seed(0)
      xs = np.random.normal(0, 1, 200)
      model.fit(xs)
      cov = fisher(model)
      assert np.isclose(cov[mu][mu], 0.00521652579559)
  
  
new_contents: 
  import numpy as np
  from tensorprob import Model, Normal, Parameter, fisher
  
  def test_fisher():
      with Model() as model:
  
          mu = Parameter()
          sigma = Parameter(lower=0)
          X = Normal(mu, sigma)
  
      model.observed(X)
      model.initialize({
          mu: 2,
          sigma: 2,
      })
      np.random.seed(0)
      xs = np.random.normal(0, 1, 200)
      model.fit(xs)
      cov = fisher(model)
      # TODO(ibab) Make this more robust and useful
      # This is the result I got when running this
      assert np.isclose(cov[mu][mu], 0.00521652579559)
  
  
subject: Add comment to test_stats.py