code: def _filter_and_mask(
      imgs,
      mask_img_,
      parameters,
      memory_level=0,
      memory=Memory(location=None),
      verbose=0,
      confounds=None,
      sample_mask=None,
      copy=True,
      dtype=None,
  ):
      """Extract representative time series using given mask.
  
      Parameters
      ----------
      imgs : 3D/4D Niimg-like object
          Images to be masked. Can be 3-dimensional or 4-dimensional.
  
      For all other parameters refer to NiftiMasker documentation.
  
      Returns
      -------
      signals : 2D numpy array
          Signals extracted using the provided mask. It is a scikit-learn
          friendly 2D array with shape n_sample x n_features.
  
      """
      # Convert input to niimg to check shape.
      # This must be repeated after the shape check because check_niimg will
      # coerce 5D data to 4D, which we don't want.
      temp_imgs = _utils.check_niimg(imgs)
  
      # Raise warning if a 3D niimg is provided.
      if temp_imgs.ndim == 3:
          warnings.warn(
              "Starting in version 0.12, 3D images will be transformed to "
              "1D arrays. "
              "Until then, 3D images will be coerced to 2D arrays, with a "
              "singleton first dimension representing time.",
              DeprecationWarning,
          )
  
      imgs = _utils.check_niimg(imgs, atleast_4d=True, ensure_ndim=4)
  
      # Check whether resampling is truly necessary. If so, crop mask
      # as small as possible in order to speed up the process
  
      if not _utils.niimg_conversions.check_same_fov(imgs, mask_img_):
          warnings.warn(
              "imgs are being resampled to the mask_img resolution. "
              "This process is memory intensive. You might want to provide "
              "a target_affine that is equal to the affine of the imgs "
              "or resample the mask beforehand "
              "to save memory and computation time.",
              UserWarning,
          )
          parameters = copy_object(parameters)
          # now we can crop
          mask_img_ = image.crop_img(mask_img_, copy=False)
          parameters["target_shape"] = mask_img_.shape
          parameters["target_affine"] = mask_img_.affine
  
      data, affine = _filter_and_extract(
          imgs,
          _ExtractionFunctor(mask_img_),
          parameters,
          memory_level=memory_level,
          memory=memory,
          verbose=verbose,
          confounds=confounds,
          sample_mask=sample_mask,
          copy=copy,
          dtype=dtype,
      )
      # For _later_: missing value removal or imputing of missing data
      # (i.e. we want to get rid of NaNs, if smoothing must be done
      # earlier)
      # Optionally: 'doctor_nan', remove voxels with NaNs, other option
      # for later: some form of imputation
      return data
  
imports: import warnings
  from copy import copy
  from functools import partial
  from joblib import Memory
  from nilearn import _utils, image, masking
  from nilearn.maskers._utils import compute_middle_image
  from nilearn.maskers.base_masker import BaseMasker, _filter_and_extract
label_desc: Use image node_id 2 for calling a global function with example usage: _filter_and_mask(imgs, mask_img_, parameters, memory_level, memory, verbose, confounds, sample_mask, copy, dtype) and returns: data