code: def generate_X_y(par, seed):
      ns, nf = par["ns"], par["nf"]
  
      drng = np.random.default_rng(seed)
      data = drng.uniform(-1, 1, size=(ns, nf))
      resp = (data > 0) @ (2 ** np.arange(nf))
  
      return data, resp
  
imports: import dpctl
  import dpctl.tensor
  import numpy
  from mpi4py import MPI
  from sklearnex.spmd.ensemble import RandomForestClassifier
label_desc: Use image node_id 1 for calling a global function with example usage: generate_X_y(par, seed) and returns: data,  resp