class_docstr: Estimator for classifying pixels.
  
  Parameters
  ----------
  clf : classifier object, optional
      classifier object, exposing a ``fit`` and a ``predict`` method as in
      scikit-learn's API, for example an instance of
      ``RandomForestClassifier`` or ``LogisticRegression`` classifier.
  features_func : function, optional
      function computing features on all pixels of the image, to be passed
      to the classifier. The output should be of shape
      ``(m_features, *labels.shape)``. If None,
      :func:`skimage.feature.multiscale_basic_features` is used.
  
  Methods
  -------
  compute_features
  fit
  predict
code: def compute_features(self, image):
      if self.features_func is None:
          self.features_func = multiscale_basic_features
      self.features = self.features_func(image)
  
file_path: scikit-image/skimage/future/trainable_segmentation.py
imports: from skimage.feature import multiscale_basic_features
label_desc: Use image node_id 2 for calling the TrainableSegmenter obj's underlying member method code with example usage: obj.compute_features(image) without return types