docstr: Returns an object of type AttributionPriorExplainer.
  Args:
      random_alpha: Whether or not the interpolation constant
                    should be drawn uniformly at random from U[0, 1], or
                    evenly spaced between [0, 1] with k points, where
                    k is the number of background reference used during training.
  Returns:
      The newly created object.
code: def __init__(self, random_alpha=True):
      """
      Returns an object of type AttributionPriorExplainer.
      Args:
          random_alpha: Whether or not the interpolation constant
                        should be drawn uniformly at random from U[0, 1], or
                        evenly spaced between [0, 1] with k points, where
                        k is the number of background reference used during training.
      Returns:
          The newly created object."""
      self.random_alpha = random_alpha
      return
  
file_path: attributionpriors/attributionpriors/ops.py
imports: import tensorflow
  import numpy
label_desc: Use image node_id 1 to create a new AttributionPriorExplainer object from inherited base classes: object with example: obj = AttributionPriorExplainer(random_alpha)