class_docstr: Implements Guided IG implementation for Tensorflow 1.
  
  https://arxiv.org/TBD
class_docstr_tok: ["Implements","Guided","IG","implementation","for","Tensorflow","1.","https",":","\/\/arxiv.org\/TBD"]
docstr: Returns Guided IG attributions.
  
  Args:
    x_value: Input value, not batched.
    feed_dict: (Optional) feed dictionary to pass to the session.run call.
    x_baseline: Baseline value used in integration. Defaults to 0.
    x_steps: the number of Riemann sum steps for path integral approximation.
    fraction: the fraction of features [0, 1] that should be selected and
      changed at every approximation step. E.g., value `0.25` means that 25% of
      the input features with smallest gradients are selected and changed at
      every step.
    max_dist: the relative maximum L1 distance [0, 1] that any feature can
      deviate from the straight line path. Value `0` allows no deviation and;
      therefore, corresponds to the Integrated Gradients method that is
      calculated on the straight-line path. Value `1` corresponds to the
      unbounded Guided IG method, where the path can go through any point within
      the baseline-input hyper-rectangular.
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code: def GetMask(
      self,
      x_value,
      feed_dict=None,
      x_baseline=None,
      x_steps=200,
      fraction=0.25,
      max_dist=0.02,
  ):
      """Returns Guided IG attributions.
  
      Args:
        x_value: Input value, not batched.
        feed_dict: (Optional) feed dictionary to pass to the session.run call.
        x_baseline: Baseline value used in integration. Defaults to 0.
        x_steps: the number of Riemann sum steps for path integral approximation.
        fraction: the fraction of features [0, 1] that should be selected and
          changed at every approximation step. E.g., value `0.25` means that 25% of
          the input features with smallest gradients are selected and changed at
          every step.
        max_dist: the relative maximum L1 distance [0, 1] that any feature can
          deviate from the straight line path. Value `0` allows no deviation and;
          therefore, corresponds to the Integrated Gradients method that is
          calculated on the straight-line path. Value `1` corresponds to the
          unbounded Guided IG method, where the path can go through any point within
          the baseline-input hyper-rectangular.
      """
      return self.core_instance.GetMask(
          x_value,
          self.call_model_function,
          call_model_args=feed_dict,
          x_baseline=x_baseline,
          x_steps=x_steps,
          fraction=fraction,
          max_dist=max_dist,
      )
  
imports: from .base import TF1CoreSaliency
  from ..core import guided_ig
label_desc: Use image node_id 2 for calling the GuidedIG obj's underlying member method code with example usage: obj.GetMask(x_value, feed_dict, x_baseline, x_steps, fraction, max_dist) and returns: self