class_docstr: Uses Shapley values to explain any machine learning model or python function.
  
  This is the primary explainer interface for the SHAP library. It takes any combination
  of a model and masker and returns a callable subclass object that implements
  the particular estimation algorithm that was chosen.
docstr: Write the explainer to the given file stream.
          
docstr_tok: ["Write","the","explainer","to","the","given","file","stream","."]
code: def save(self, out_file, model_saver=".save", masker_saver=".save"):
      """Write the explainer to the given file stream."""
      super().save(out_file)
      with Serializer(out_file, "shap.Explainer", version=0) as s:
          s.save("model", self.model, model_saver)
          s.save("masker", self.masker, masker_saver)
          s.save("link", self.link)
  
imports: import copy
  import time
  import numpy
  import pandas
  import scipy.sparse
  from ..None import explainers, links, maskers, models
  from .._explanation import Explanation
  from .._serializable import Deserializer, Serializable, Serializer
  from ..maskers import Masker
  from ..models import Model
  from ..utils import safe_isinstance, show_progress
  from ..utils._exceptions import InvalidAlgorithmError
  from ..utils.transformers import is_transformers_lm
label_desc: Use image node_id 6 for calling the Explainer obj's underlying member method code with example usage: obj.save(out_file, model_saver, masker_saver) without return types