class_docstr: Base class for NiftiMaskers.
class_docstr_tok: ["Base","class","for","NiftiMaskers","."]
docstr: Transform the 2D data matrix back to an image in brain space.
  
  This step only performs spatial unmasking,
  without inverting any additional processing performed by ``transform``,
  such as temporal filtering or smoothing.
  
  Parameters
  ----------
  X : 1D/2D :obj:`numpy.ndarray`
      Signal for each element in the mask.
      If a 1D array is provided, then the shape should be
      (number of elements,), and a 3D img will be returned.
      If a 2D array is provided, then the shape should be
      (number of scans, number of elements), and a 4D img will be
      returned.
      See :ref:`extracting_data`.
  
  Returns
  -------
  img : Transformed image in brain space.
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code: def inverse_transform(self, X):
      """Transform the 2D data matrix back to an image in brain space.
  
      This step only performs spatial unmasking,
      without inverting any additional processing performed by ``transform``,
      such as temporal filtering or smoothing.
  
      Parameters
      ----------
      X : 1D/2D :obj:`numpy.ndarray`
          Signal for each element in the mask.
          If a 1D array is provided, then the shape should be
          (number of elements,), and a 3D img will be returned.
          If a 2D array is provided, then the shape should be
          (number of scans, number of elements), and a 4D img will be
          returned.
          See :ref:`extracting_data`.
  
      Returns
      -------
      img : Transformed image in brain space.
  
      """
      self._check_fitted()
  
      img = self._cache(masking.unmask)(X, self.mask_img_)
      # Be robust again memmapping that will create read-only arrays in
      # internal structures of the header: remove the memmaped array
      try:
          img._header._structarr = np.array(
              img._header._structarr
          ).copy()
      except Exception:
          pass
      return img
  
file_path: nilearn/nilearn/maskers/base_masker.py
imports: import abc
  import warnings
  import numpy
  from joblib import Memory
  from sklearn.base import BaseEstimator, TransformerMixin
  from nilearn.image import high_variance_confounds
  from ..None import _utils, image, masking, signal
  from .._utils import stringify_path
  from .._utils.cache_mixin import CacheMixin, cache
  from .._utils.class_inspect import enclosing_scope_name
label_desc: Use image node_id 4 for calling the BaseMasker obj's underlying member method code with example usage: obj.inverse_transform(X) and returns: img