code: def load(file):
      """Load fitted SRM from .npz file.
  
      Parameters
      ----------
  
      file : str, file-like object, or pathlib.Path
          The .npz file to read containing fitted SRM saved using srm.save
  
      Returns
      --------
  
      srm : fitted SRM model
      """
  
      # Load file and extract SRM attributes
      loaded = np.load(file)
      w_ = [s for s in loaded["w_"]]
      s_ = loaded["s_"]
      sigma_s_ = loaded["sigma_s_"]
      mu_ = [s for s in loaded["mu_"]]
      rho2_ = loaded["rho2_"]
      features, n_iter, rand_seed = loaded["kwargs"]
  
      # Initialize new SRM object and attach loaded attributes
      srm = SRM(n_iter=n_iter, features=features, rand_seed=rand_seed)
      srm.w_ = w_
      srm.s_ = s_
      srm.sigma_s_ = sigma_s_
      srm.mu_ = mu_
      srm.rho2_ = rho2_
  
      return srm
  
imports: import logging
  import numpy
  import scipy
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.utils import assert_all_finite
  from sklearn.exceptions import NotFittedError
  from mpi4py import MPI
  import sys
label_desc: Use image node_id 2 for calling a global function with example usage: load(file) and returns: srm