class_docstr: XRAI Interpreter that uses Guided IG as the base attribution.
class_docstr_tok: ["XRAI","Interpreter","that","uses","Guided","IG","as","the","base","attribution","."]
code: def make_saliency_call(
      self,
      saliency_object: saliency.core.CoreSaliency,
      x_value: np.ndarray,
      call_model_function: CallModelFunction,
      extra_saliency_params: dict[str, Any],
  ) -> np.ndarray:
      gig_object = saliency.core.GuidedIG()
      gig_saliency = gig_object.GetMask(
          x_value=x_value,
          call_model_function=call_model_function,
          **extra_saliency_params,
      )
  
      xrai_params = saliency.core.XRAIParameters(
          steps=extra_saliency_params["x_steps"], algorithm="fast"
      )
      xrai_output = cast(
          saliency.core.XRAI, saliency_object
      ).GetMaskWithDetails(
          x_value=x_value,
          call_model_function=call_model_function,
          base_attribution=gig_saliency,
          extra_parameters=xrai_params,
      )
      return xrai_output.attribution_mask
  
imports: import abc
  from collections.abc import Callable
  from typing import Any, NamedTuple, Optional, cast
  from absl import logging
  from lit_nlp.api import components
  from lit_nlp.api import dataset
  from lit_nlp.api import model
  from lit_nlp.api import types
  from lit_nlp.lib import image_utils
  from lit_nlp.lib import utils
  import numpy
  import saliency
label_desc: Use image node_id 4 for calling the XRAIGIG obj's underlying member method code with example usage: obj.make_saliency_call(saliency_object, x_value, call_model_function, extra_saliency_params) and returns: xrai_output