code: def _mask_and_reduce_single(
      masker,
      img,
      confound,
      reduction_ratio=None,
      n_samples=None,
      memory=None,
      memory_level=0,
      random_state=None,
  ):
      """Implement multiprocessing from MaskReducer."""
      this_data = masker.transform(img, confound)
      # Now get rid of the img as fast as possible, to free a
      # reference count on it, and possibly free the corresponding
      # data
      del img
      random_state = check_random_state(random_state)
  
      data_n_samples = this_data.shape[0]
      if reduction_ratio is None:
          assert n_samples is not None
          n_samples = min(n_samples, data_n_samples)
      else:
          n_samples = int(ceil(data_n_samples * reduction_ratio))
  
      U, S, V = cache(
          _fast_svd,
          memory,
          memory_level=memory_level,
          func_memory_level=3,
      )(this_data.T, n_samples, random_state=random_state)
      U = U.T.copy()
      U = U * S[:, np.newaxis]
      return U
  
imports: import glob
  import itertools
  from math import ceil
  import numpy
  from joblib import Memory, Parallel, delayed
  from scipy import linalg
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.linear_model import LinearRegression
  from sklearn.utils import check_random_state
  from sklearn.utils.extmath import randomized_svd, svd_flip
  import nilearn
  from nilearn._utils.masker_validation import check_embedded_masker
  from nilearn.maskers import NiftiMapsMasker
  from .._utils import fill_doc
  from .._utils.cache_mixin import CacheMixin, cache
  from .._utils.niimg import safe_get_data
  from .._utils.niimg_conversions import resolve_globbing
  from ..signal import row_sum_of_squares
label_desc: Use image node_id 3 for calling a global function with example usage: _mask_and_reduce_single(masker, img, confound, reduction_ratio, n_samples, memory, memory_level, random_state) and returns: U