class_docstr: Dictionary of outputs from a single run of XRAI.GetMaskWithDetails.
class_docstr_tok: ["Dictionary","of","outputs","from","a","single","run","of","XRAI.GetMaskWithDetails","."]
code: def __init__(self, attribution_mask):
      # The saliency mask of individual input features. For an [HxWx3] image, the
      # returned attribution is [H,W,1] float32 array. Where HxW are the
      # dimensions of the image.
      self.attribution_mask = attribution_mask
      # Baselines that were used for IG calculation. The shape is [B,H,W,C], where
      # B is the number of baselines, HxWxC are the image dimensions.
      self.baselines = None
      # TODO(tolgab) add return IG error functionality from XRAI API
      # The average error of the IG attributions as a percentage. The error can be
      # decreased by increasing the number of steps (see XraiParameters.steps).
      # self.error = None
      # TODO(tolgab) add return baseline predictions functionality from XRAI API
      # Predictions for the baselines that were used for the calculation of IG
      # attributions. The value is set only when
      # XraiParameters.return_baseline_predictions is set to True.
      # self.baseline_predictions = None
      # IG attributions for individual baselines. The value is set only when
      # XraiParameters.ig_attributions is set to True. For the dimensions of the
      # output see XraiParameters.return_ig_for_every _step.
      self.ig_attribution = None
      # The result of the XRAI segmentation. The value is set only when
      # XraiParameters.return_xrai_segments is set to True. For the dimensions of
      # the output see XraiParameters.flatten_xrai_segments.
      self.segments = None
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import logging
  from .base import CoreSaliency
  from .integrated_gradients import IntegratedGradients
  import numpy
  from skimage import segmentation
  from skimage.morphology import dilation
  from skimage.morphology import disk
  from skimage.transform import resize
label_desc: Use image node_id 1 to create a new XRAIOutput object from inherited base classes: object with example: obj = XRAIOutput(attribution_mask)