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
docstr: Apply mask, spatial and temporal preprocessing.
  
  Parameters
  ----------
  imgs : :obj:`list` of Niimg-like objects
      See :ref:`extracting_data`.
      Data to be preprocessed
  
  confounds : CSV file or 2D :obj:`numpy.ndarray` or                 :obj:`pandas.DataFrame`, optional
      This parameter is passed to signal.clean. Please see the
      corresponding documentation for details.
  
  sample_mask : :obj:`list` of 1D :obj:`numpy.ndarray`, optional
      List of sample_mask (1D arrays) if scrubbing motion outliers.
      Must be of same length than imgs_list.
  
          .. versionadded:: 0.8.0
  
  Returns
  -------
  data : :obj:`list` of :obj:`numpy.ndarray`
      preprocessed images
  
  Warns
  -----
  DeprecationWarning
      If 3D niimg inputs are provided, the current behavior
      (adding a singleton dimension to produce 2D arrays) is deprecated.
      Starting in version 0.12, 1D arrays will be returned for 3D
      inputs.
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code: def transform(self, imgs, confounds=None, sample_mask=None):
      """Apply mask, spatial and temporal preprocessing.
  
          Parameters
          ----------
          imgs : :obj:`list` of Niimg-like objects
              See :ref:`extracting_data`.
              Data to be preprocessed
  
          confounds : CSV file or 2D :obj:`numpy.ndarray` or \
                  :obj:`pandas.DataFrame`, optional
              This parameter is passed to signal.clean. Please see the
              corresponding documentation for details.
  
          sample_mask : :obj:`list` of 1D :obj:`numpy.ndarray`, optional
              List of sample_mask (1D arrays) if scrubbing motion outliers.
              Must be of same length than imgs_list.
  
                  .. versionadded:: 0.8.0
  
          Returns
          -------
          data : :obj:`list` of :obj:`numpy.ndarray`
              preprocessed images
  
          Warns
          -----
          DeprecationWarning
              If 3D niimg inputs are provided, the current behavior
              (adding a singleton dimension to produce 2D arrays) is deprecated.
              Starting in version 0.12, 1D arrays will be returned for 3D
              inputs.
  
          """
      self._check_fitted()
      if not hasattr(imgs, "__iter__") or isinstance(imgs, str):
          return self.transform_single_imgs(imgs)
  
      return self.transform_imgs(
          imgs,
          confounds=confounds,
          sample_mask=sample_mask,
          n_jobs=self.n_jobs,
      )
  
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 4 for calling the MultiNiftiMasker obj's underlying member method code with example usage: obj.transform(imgs, confounds, sample_mask) and returns: self, self