class_docstr: Simply returns random (normally distributed) feature attributions.
  
  This is only for benchmark comparisons. It supports both fully random attributions and random
  attributions that are constant across all explanations.
code: def __init__(
      self,
      model,
      masker,
      link=links.identity,
      feature_names=None,
      linearize_link=True,
      constant=False,
      **call_args,
  ):
      super().__init__(
          model,
          masker,
          link=link,
          linearize_link=linearize_link,
          feature_names=feature_names,
      )
  
      if not isinstance(model, Model):
          self.model = Model(model)
  
      for arg in call_args:
          self.__call__.__kwdefaults__[arg] = call_args[arg]
  
      self.constant = constant
      self.constant_attributions = None
  
file_path: shap/shap/explainers/other/_random.py
imports: import numpy
  from shap import links
  from shap.models import Model
  from shap.utils import MaskedModel
  from .._explainer import Explainer
label_desc: Use image node_id 1 to create a new Random object from inherited base classes: Explainer with example: obj = Random(model, masker, link, feature_names, linearize_link, constant)