class_docstr: Extract data from a SurfaceImage.
class_docstr_tok: ["Extract","data","from","a","SurfaceImage","."]
docstr: Prepare and perform signal extraction from regions.
  
  Parameters
  ----------
  img : SurfaceImage object
      Mesh and data for both hemispheres.
  
  y : None
      This parameter is unused. It is solely included for scikit-learn
      compatibility.
  
  Returns
  -------
  numpy.ndarray
      Signal for each element.
      shape: (img data shape, total number of vertices)
docstr_tok: ["Prepare","and","perform","signal","extraction","from","regions",".","Parameters","--","--","--","--","--","img",":","SurfaceImage","object","Mesh","and","data","for","both","hemispheres",".","y",":","None","This","parameter","is","unused",".","It","is","solely","included","for","scikit-learn","compatibility",".","Returns","--","--","--","-","numpy.ndarray","Signal","for","each","element",".","shape",":","(","img","data","shape",",","total","number","of","vertices",")"]
code: def fit_transform(
      self,
      img: SurfaceImage,
      y: Any = None,
      confounds: pd.DataFrame | None = None,
      sample_mask: np.ndarray | None = None,
  ) -> np.ndarray:
      """Prepare and perform signal extraction from regions.
  
      Parameters
      ----------
      img : SurfaceImage object
          Mesh and data for both hemispheres.
  
      y : None
          This parameter is unused. It is solely included for scikit-learn
          compatibility.
  
      Returns
      -------
      numpy.ndarray
          Signal for each element.
          shape: (img data shape, total number of vertices)
      """
      del y
      return self.fit(img).transform(img, confounds, sample_mask)
  
imports: from __future__ import annotations
  from typing import Any
  import numpy
  import pandas
  from joblib import Memory
  from sklearn.base import BaseEstimator, TransformerMixin
  from nilearn import signal
  from nilearn._utils.cache_mixin import CacheMixin, cache
  from nilearn._utils.class_inspect import get_params
  from nilearn.experimental.surface._surface_image import PolyMesh, SurfaceImage
label_desc: Use image node_id 6 for calling the SurfaceMasker obj's underlying member method code with example usage: obj.fit_transform(img, y, confounds, sample_mask) and returns: self