class_docstr: Extract data from a SurfaceImage.
class_docstr_tok: ["Extract","data","from","a","SurfaceImage","."]
code: def __init__(
      self,
      mask_img=None,
      standardize=False,
      standardize_confounds=True,
      detrend=False,
      high_variance_confounds=False,
      low_pass=None,
      high_pass=None,
      t_r=None,
      memory_level=1,
      memory=Memory(location=None),
      **kwargs,
  ):
      self.mask_img = mask_img
      self.standardize = standardize
      self.standardize_confounds = standardize_confounds
      self.high_variance_confounds = high_variance_confounds
      self.detrend = detrend
      self.low_pass = low_pass
      self.high_pass = high_pass
      self.t_r = t_r
  
      self.memory = memory
      self.memory_level = memory_level
      self._shelving = False
      self.clean_kwargs = {
          k[7:]: v for k, v in kwargs.items() if k.startswith("clean__")
      }
  
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 1 to create a new SurfaceMasker object from inherited base classes: BaseEstimator, TransformerMixin, CacheMixin with example: obj = SurfaceMasker(mask_img, standardize, standardize_confounds, detrend, high_variance_confounds, low_pass, high_pass, t_r, memory_level, memory)