class_docstr: Implementation of the Model Parameter Randomisation Test (MPRT) by Adebayo et al., 2018.
  
  The MPRT measures the distance between the original attribution and a newly computed
  attribution throughout the process of cascadingly/ independently randomizing the model
  parameters of one layer at a time.
  
  Assumptions:
      - In the original paper multiple distance measures are taken: Spearman rank correlation (with and without abs),
      HOG and SSIM. We have set Spearman as the default value.
  
  References:
      1) Julius Adebayo et al.: "Sanity Checks for Saliency Maps." NeurIPS (2018): 9525-9536.
  
  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.
code: def recompute_last_correlation_per_sample(
      self,
  ) -> List[float]:
      assert isinstance(self.evaluation_scores, dict), (
          "To compute the last correlation coefficient per sample for "
          "enhanced Model Parameter Randomisation Test, 'evaluation_scores' "
          "must be of type dict."
      )
      # Return the correlation coefficient of the fully randomised model
      # (excluding the non-randomised correlation).
      corr_coeffs = list(self.evaluation_scores.values())[-1]
      corr_coeffs = [float(c) for c in corr_coeffs]
      return corr_coeffs
  
imports: import sys
  import warnings
  from typing import Any, Callable, Collection, Dict, List, Optional, Union, Tuple, Generator, Iterable
  import numpy
  from tqdm.auto import tqdm
  from sklearn.utils import gen_batches
  from quantus.functions.similarity_func import correlation_spearman
  from quantus.helpers import asserts, warn
  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 4 for calling the MPRT obj's underlying member method code with example usage: obj.recompute_last_correlation_per_sample() and returns: corr_coeffs