code: def norm_f(x, y):
      """Frobenious norm (squared sum) of difference between two arrays"""
      d = ((x - y) ** 2).sum()
      return np.sqrt(d)
  
imports: import warnings
  import numpy
  from numpy.testing import assert_almost_equal, assert_allclose
  import scipy.sparse
  import pytest
  from statsmodels.stats.correlation_tools import corr_nearest, corr_clipped, cov_nearest, _project_correlation_factors, corr_nearest_factor, _spg_optim, corr_thresholded, cov_nearest_factor_homog, FactoredPSDMatrix
  from statsmodels.tools.testing import Holder
label_desc: Use image node_id 1 for calling a global function with example usage: norm_f(x, y) and returns: np