class_docstr: Class for masking of Niimg-like objects.
  
  MultiNiftiMasker is useful when dealing with image sets from multiple
  subjects. Use case: integrates well with decomposition by MultiPCA and
  CanICA (multi-subject models)
  
  Parameters
  ----------
  mask_img : Niimg-like object
      See :ref:`extracting_data`.
      Mask of the data. If not given, a mask is computed in the fit step.
      Optional parameters can be set using mask_args and mask_strategy to
      fine tune the mask extraction.
  %(smoothing_fwhm)s
  %(standardize_maskers)s
  %(standardize_confounds)s
  high_variance_confounds : :obj:`bool`, default=False
      If True, high variance confounds are computed on provided image with
      :func:`nilearn.image.high_variance_confounds` and default parameters
      and regressed out.
  %(detrend)s
  %(low_pass)s
  %(high_pass)s
  %(t_r)s
  target_affine : 3x3 or 4x4 :obj:`numpy.ndarray`, optional
      This parameter is passed to image.resample_img. Please see the
      related documentation for details.
  
  target_shape : 3-:obj:`tuple` of :obj:`int`, optional
      This parameter is passed to image.resample_img. Please see the
      related documentation for details.
  
  %(mask_strategy)s
  
      .. note::
          Depending on this value, the mask will be computed from
          :func:`nilearn.masking.compute_multi_background_mask`,
          :func:`nilearn.masking.compute_multi_epi_mask`, or
          :func:`nilearn.masking.compute_multi_brain_mask`.
  
      Default='background'.
  
  mask_args : :obj:`dict`, optional
      If mask is None, these are additional parameters passed to
      masking.compute_background_mask or masking.compute_epi_mask
      to fine-tune mask computation. Please see the related documentation
      for details.
  
  dtype : {dtype, "auto"}, optional
      Data type toward which the data should be converted. If "auto", the
      data will be converted to int32 if dtype is discrete and float32 if it
      is continuous.
  %(memory)s
  %(memory_level)s
  %(n_jobs)s
  %(verbose0)s
  %(masker_kwargs)s
  
  Attributes
  ----------
  mask_img_ : :obj:`nibabel.nifti1.Nifti1Image`
      The mask of the data.
  
  affine_ : 4x4 :obj:`numpy.ndarray`
      Affine of the transformed image.
  
  n_elements_ : :obj:`int`
      The number of voxels in the mask.
  
      .. versionadded:: 0.9.2
  
  See Also
  --------
  nilearn.image.resample_img: image resampling
  nilearn.masking.compute_epi_mask: mask computation
  nilearn.masking.apply_mask: mask application on image
  nilearn.signal.clean: confounds removal and general filtering of signals
code: def __init__(
      self,
      mask_img=None,
      smoothing_fwhm=None,
      standardize=False,
      standardize_confounds=True,
      detrend=False,
      high_variance_confounds=False,
      low_pass=None,
      high_pass=None,
      t_r=None,
      target_affine=None,
      target_shape=None,
      mask_strategy="background",
      mask_args=None,
      dtype=None,
      memory=Memory(location=None),
      memory_level=0,
      n_jobs=1,
      verbose=0,
      **kwargs,
  ):
      super().__init__(
          # Mask is provided or computed
          mask_img=mask_img,
          smoothing_fwhm=smoothing_fwhm,
          standardize=standardize,
          standardize_confounds=standardize_confounds,
          high_variance_confounds=high_variance_confounds,
          detrend=detrend,
          low_pass=low_pass,
          high_pass=high_pass,
          t_r=t_r,
          target_affine=target_affine,
          target_shape=target_shape,
          mask_strategy=mask_strategy,
          mask_args=mask_args,
          dtype=dtype,
          clean_kwargs={
              k[7:]: v
              for k, v in kwargs.items()
              if k.startswith("clean__")
          },
          memory=memory,
          memory_level=memory_level,
          verbose=verbose,
          **kwargs,
      )
      self.n_jobs = n_jobs
      self._shelving = False
  
file_path: nilearn/nilearn/maskers/multi_nifti_masker.py
imports: import collections.abc
  import itertools
  import warnings
  from functools import partial
  from joblib import Memory, Parallel, delayed
  from nilearn import _utils, image, masking
  from nilearn.maskers._utils import compute_middle_image
  from nilearn.maskers.nifti_masker import NiftiMasker, _filter_and_mask
label_desc: Use image node_id 1 to create a new MultiNiftiMasker object from inherited base classes: NiftiMasker, _utils with example: obj = MultiNiftiMasker(mask_img, smoothing_fwhm, standardize, standardize_confounds, detrend, high_variance_confounds, low_pass, high_pass, t_r, target_affine, target_shape, mask_strategy, mask_args, dtype, memory, memory_level, n_jobs, verbose)