code: def __init__(
      self, background_dataset, batch_size, random_alpha=True, k=1
  ):
      self.random_alpha = random_alpha
      self.k = k
      self.batch_size = batch_size
      self.ref_sampler = DataLoader(
          dataset=background_dataset,
          batch_size=batch_size * k,
          shuffle=True,
          drop_last=True,
      )
      return
  
file_path: attributionpriors/attributionpriors/pytorch_ops_cpu.py
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)