code: def test_update_transform():
      n = 150
      m = 10
      y = np.random.RandomState(1).normal(size=n) + (
          np.arange(1, n + 1) % 100 / 30
      )
  
      train, test = y[:100], y[100:]
  
      trans = FourierFeaturizer(m=m, k=5).fit(train)
      _, xreg = trans.transform(train)
  
      # Now update with the test set and show the xreg is diff
      yt, Xt = trans.update_and_transform(test, X=None)
      assert yt is test
      assert Xt.shape[0] == test.shape[0]
      assert trans.n_ == y.shape[0]
  
      # Now assert that if we do a vanilla transform with no n_periods, the last
      # 50 are the same as the Xt we just got and the first 100 are the same as
      # we got earlier
      _, xreg2 = trans.transform(y)
      assert_array_almost_equal(xreg2[:100], xreg)
      assert_array_almost_equal(xreg2[100:], Xt)
  
file_path: pmdarima/pmdarima/preprocessing/exog/tests/test_fourier.py
imports: import numpy
  from numpy.testing import assert_array_almost_equal
  from pmdarima.preprocessing.exog import FourierFeaturizer
  from pmdarima.compat.pytest import pytest_error_str
  import pmdarima
  import pytest
label_desc: Use image node_id 2 for calling a global function with example usage: test_update_transform() without return types