class_docstr: Base class for LIME Scikit-Image wrapper
  
  
  Args:
      target_fn: callable function or class instance
      target_params: dict, parameters to pass to the target_fn
  
  
  'target_params' takes parameters required to instanciate the
      desired Scikit-Image class/model
class_docstr_tok: ["Base","class","for","LIME","Scikit-Image","wrapper","Args",":","target_fn",":","callable","function","or","class","instance","target_params",":","dict",",","parameters","to","pass","to","the","target_fn","'target_params","'","takes","parameters","required","to","instanciate","the","desired","Scikit-Image","class\/model"]
docstr: Sets the parameters of this estimator.
  Args:
      **params: Dictionary of parameter names mapped to their values.
  
  Raises :
      ValueError: if any parameter is not a valid argument
          for the target function
docstr_tok: ["Sets","the","parameters","of","this","estimator",".","Args",":","*","*","params",":","Dictionary","of","parameter","names","mapped","to","their","values",".","Raises",":","ValueError",":","if","any","parameter","is","not","a","valid","argument","for","the","target","function"]
code: def set_params(self, **params):
      """Sets the parameters of this estimator.
      Args:
          **params: Dictionary of parameter names mapped to their values.
  
      Raises :
          ValueError: if any parameter is not a valid argument
              for the target function
      """
      self._check_params(params)
      self.target_params = params
  
file_path: lime/lime/wrappers/scikit_image.py
imports: import types
  from lime.utils.generic_utils import has_arg
  from skimage.segmentation import felzenszwalb, slic, quickshift
label_desc: Use image node_id 3 for calling the BaseWrapper obj's underlying member method code with example usage: obj.set_params() without return types