class_docstr: Implementation of the Smooth MPRT by Hedström et al., 2023.
  
  The Smooth Model Parameter Randomisation adds a "denoising" preprocessing step to the original MPRT,
  where the explanations are averaged over N noisy samples before the similarity between the original-
  and fully random model's explanations is measured.
  
  References:
      1) Hedström, Anna, et al. "Sanity Checks Revisited: An Exploration to Repair the Model Parameter
      Randomisation Test." XAI in Action: Past, Present, and Future Applications. 2023.
  
  Attributes:
      -  _name: The name of the metric.
      - _data_applicability: The data types that the metric implementation currently supports.
      - _models: The model types that this metric can work with.
      - score_direction: How to interpret the scores, whether higher/ lower values are considered better.
      - evaluation_category: What property/ explanation quality that this metric measures.
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code: def generate_a_batches(self, a_full_dataset):
      for batch in gen_batches(len(a_full_dataset), self.batch_size):
          yield a_full_dataset[batch.start : batch.stop]
  
imports: import sys
  import warnings
  from typing import Any, Callable, Collection, Dict, List, Optional, Union, Tuple, Generator, Iterable
  from importlib import util
  import numpy
  import quantus
  from tqdm.auto import tqdm
  from sklearn.utils import gen_batches
  from scipy import stats
  from quantus.functions.similarity_func import correlation_spearman
  from quantus.helpers import asserts, warn, utils
  from quantus.helpers.enums import DataType, EvaluationCategory, ModelType, ScoreDirection
  from quantus.helpers.model.model_interface import ModelInterface
  from quantus.metrics.base import Metric
label_desc: Use image node_id 8 for calling the SmoothMPRT obj's underlying member method code with example usage: obj.generate_a_batches(a_full_dataset) without return types