code: def data(request) -> ArrayLike:
      rs = np.random.RandomState([3894830, 432841, 323297, 8927821])
      ndim, use_pandas = request.param
      burn = 100
      size: tuple[int, ...] = (500 + burn,)
      if ndim == 2:
          size += (3,)
      e = rs.standard_normal(size)
      if ndim == 1:
          phi = rs.uniform(0, 0.9)
          for i in range(1, size[0]):
              e[i] += e[i - 1] * phi
      else:
          phi_arr = np.diag(rs.uniform(0, 0.9, size[1]))
          for i in range(1, size[0]):
              e[i] += e[i - 1] @ phi_arr
      e = e[burn:]
      if use_pandas:
          if ndim == 1:
              return pd.Series(e, name="x")
          else:
              return pd.DataFrame(
                  e, columns=[f"x{i}" for i in range(e.shape[1])]
              )
      return e
  
file_path: arch/arch/tests/covariance/test_covariance.py
imports: from itertools import product
  import numpy
  from numpy.testing import assert_allclose
  import pandas
  import pytest
  from arch.covariance.kernel import Andrews, Bartlett, CovarianceEstimate, CovarianceEstimator, Gallant, NeweyWest, Parzen, ParzenCauchy, ParzenGeometric, ParzenRiesz, QuadraticSpectral, TukeyHamming, TukeyHanning, TukeyParzen
  from arch.typing import ArrayLike
label_desc: Use image node_id 1 for calling a global function with example usage: data(request) and returns: e, pd, pd