code: def __init__(
      self,
      background_dataset,
      batch_size,
      random_alpha=True,
      k=1,
      scale_by_inputs=True,
  ):
      self.random_alpha = random_alpha
      self.k = k
      self.scale_by_inputs = scale_by_inputs
      self.batch_size = batch_size
      self.ref_set = background_dataset
      self.ref_sampler = DataLoader(
          dataset=background_dataset,
          batch_size=batch_size * k,
          shuffle=True,
          drop_last=True,
      )
      return
  
imports: import functools
  import operator
  import torch
  from torch.autograd import grad
  from torch.utils.data import DataLoader
label_desc: Use image node_id 1 to create a new AttributionPriorExplainer object from inherited base classes: object with example: obj = AttributionPriorExplainer(background_dataset, batch_size, random_alpha, k, scale_by_inputs)