class_docstr: Produces Attribution for random samples in TEST SET
class_docstr_tok: ["Produces","Attribution","for","random","samples","in","TEST","SET"]
code: def __init__(
      self,
      version: str,
      model: Tuple[Module, None],
      data,
      layers: List[str],
      sample_idx=None,
      custom_dataloader=None,
      random_seed: int = 2021,
  ):
      self.random_seed = random_seed
      self.version = version
  
      self.layer = layers
      self.model = model
      self.data = data
  
      assert len(layers) == 1
  
      if sample_idx is None:
          sample_idx = index_sample_data(
              self.data.test, sample_size=10000, random_seed=random_seed
          )
  
      if custom_dataloader is None:
          self.loader_samples = self.get_dataloader(
              self.data.test, sample_idx
          )
      else:
          self.loader_samples = custom_dataloader
  
      if self.model is None:
          warnings.warn(
              "Model is not provided, use load_model method to assign a nn.Module"
          )
      else:
          self.device = model.device
          self.model.eval()
  
imports: from abc import ABC, abstractmethod
  from ast import Str
  from posixpath import split
  from sys import meta_path
  import numpy
  import time
  import os
  import pickle
  from typing import Any, List, Tuple, Callable, Dict, NamedTuple
  from functools import reduce
  import warnings
  from torch.nn import Module
  import torch
  from captum.attr import LayerActivation, LayerGradientXActivation, LayerIntegratedGradients, IntegratedGradients, InputXGradient
  from sklearn.base import BaseEstimator
  from sklearn.metrics import f1_score, balanced_accuracy_score, make_scorer, matthews_corrcoef, average_precision_score
  from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedKFold
  from sklearn.ensemble import BaggingClassifier
label_desc: Use image node_id 1 to create a new Attributions object from inherited base classes: BaseAttributions with example: obj = Attributions(version, model, data, layers, sample_idx, custom_dataloader, random_seed)