code: def generate_X_y(par, coef_seed, data_seed):
      ns, nf = par["ns"], par["nf"]
  
      crng = np.random.default_rng(coef_seed)
      coef = crng.uniform(-10, 10, size=(nf,))
  
      drng = np.random.default_rng(data_seed)
      data = drng.uniform(-100, 100, size=(ns, nf))
      resp = data @ coef
  
      return data, resp, coef
  
file_path: scikit-learn-intelex/examples/sklearnex/knn_bf_regression_spmd.py
imports: from warnings import warn
  import dpctl
  import dpctl.tensor
  import numpy
  from mpi4py import MPI
  from numpy.testing import assert_allclose
  from sklearn.metrics import mean_squared_error
  from sklearnex.spmd.neighbors import KNeighborsRegressor
label_desc: Use image node_id 1 for calling a global function with example usage: generate_X_y(par, coef_seed, data_seed) and returns: data,  resp,  coef