class_docstr: This is a utility class that combines a model, a masker object, and a current input.
  
  The combination of a model, a masker object, and a current input produces a binary set
  function that can be called to mask out any set of inputs. This class attempts to be smart
  about only evaluating the model for background samples when the inputs changed (note this
  requires the masker object to have a .invariants method).
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docstr: How many binary inputs there are to toggle.
  
  By default we just match what the masker tells us. But if the masker doesn't help us
  out by giving a length then we assume is the number of data inputs.
docstr_tok: ["How","many","binary","inputs","there","are","to","toggle",".","By","default","we","just","match","what","the","masker","tells","us",".","But","if","the","masker","does","n't","help","us","out","by","giving","a","length","then","we","assume","is","the","number","of","data","inputs","."]
code: def __len__(self):
      """How many binary inputs there are to toggle.
  
      By default we just match what the masker tells us. But if the masker doesn't help us
      out by giving a length then we assume is the number of data inputs.
      """
      return self._masker_cols
  
imports: import copy
  import numpy
  import scipy.sparse
  from numba import njit
  from ..None import links
label_desc: Use image node_id 6 for calling the MaskedModel obj's underlying member method code with example usage: obj.__len__() and returns: self