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: Load an Explainer from the given file stream.
  
  Parameters
  ----------
  in_file : The file stream to load objects from.
docstr_tok: ["Load","an","Explainer","from","the","given","file","stream",".","Parameters","--","--","--","--","--","in_file",":","The","file","stream","to","load","objects","from","."]
code: def load(
      cls,
      in_file,
      model_loader=Model.load,
      masker_loader=Masker.load,
      instantiate=True,
  ):
      """Load an Explainer from the given file stream.
  
      Parameters
      ----------
      in_file : The file stream to load objects from.
      """
      if instantiate:
          return cls._instantiated_load(
              in_file,
              model_loader=model_loader,
              masker_loader=masker_loader,
          )
  
      kwargs = super().load(in_file, instantiate=False)
      with Deserializer(
          in_file, "shap.Explainer", min_version=0, max_version=0
      ) as s:
          kwargs["model"] = s.load("model", model_loader)
          kwargs["masker"] = s.load("masker", masker_loader)
          kwargs["link"] = s.load("link")
      return kwargs
  
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 7 for calling the Explainer obj's underlying member method code with example usage: obj.load(cls, in_file, model_loader, masker_loader, instantiate) and returns: kwargs, cls