code: def svd_timing(
      X,
      n_comps,
      n_iter,
      n_oversamples,
      power_iteration_normalizer="auto",
      method=None,
  ):
      """
      Measure time for decomposition
      """
      print("... running SVD ...")
      if method != "fbpca":
          gc.collect()
          t0 = time()
          U, mu, V = randomized_svd(
              X,
              n_comps,
              n_oversamples=n_oversamples,
              n_iter=n_iter,
              power_iteration_normalizer=power_iteration_normalizer,
              random_state=random_state,
              transpose=False,
          )
          call_time = time() - t0
      else:
          gc.collect()
          t0 = time()
          # There is a different convention for l here
          U, mu, V = fbpca.pca(
              X,
              n_comps,
              raw=True,
              n_iter=n_iter,
              l=n_oversamples + n_comps,
          )
          call_time = time() - t0
  
      return U, mu, V, call_time
  
imports: import gc
  import os.path
  import pickle
  from collections import defaultdict
  from time import time
  import matplotlib.pyplot
  import numpy
  import scipy
  from sklearn.datasets import fetch_20newsgroups_vectorized, fetch_lfw_people, fetch_olivetti_faces, fetch_openml, fetch_rcv1, make_low_rank_matrix, make_sparse_uncorrelated
  from sklearn.utils import gen_batches
  from sklearn.utils._arpack import _init_arpack_v0
  from sklearn.utils.extmath import randomized_svd
  from sklearn.utils.validation import check_random_state
label_desc: Use image node_id 7 for calling a global function with example usage: svd_timing(X, n_comps, n_iter, n_oversamples, power_iteration_normalizer, method) and returns: U,  mu,  V,  call_time