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: Determines if this explainer can handle the given model.
  
  This is an abstract static method meant to be implemented by each subclass.
docstr_tok: ["Determines","if","this","explainer","can","handle","the","given","model",".","This","is","an","abstract","static","method","meant","to","be","implemented","by","each","subclass","."]
code: def supports_model_with_masker(model, masker):
      """Determines if this explainer can handle the given model.
  
      This is an abstract static method meant to be implemented by each subclass.
      """
      return False
  
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 4 for calling the Explainer obj's underlying member method code with example usage: obj.supports_model_with_masker(model, masker) and returns: False