class_docstr: Implementation of the Efficient MPRT by Hedström et al., 2023.
  
  The Efficient MPRT measures replaces the layer-by-layer pairwise comparison
  between e and ˆe of MPRT by instead computing the relative rise in explanation complexity using only
  two model states, i.e., the original- and fully randomised model version
  
  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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docstr: Evaluate instance gets model and data for a single instance as input and returns the evaluation result.
  
  Parameters
  ----------
  model: ModelInterface
      A ModelInteface that is subject to explanation.
  x: np.ndarray
      The input to be evaluated on an instance-basis.
  y: np.ndarray
      The output to be evaluated on an instance-basis.
  a: np.ndarray
      The explanation to be evaluated on an instance-basis.
  s: np.ndarray
      The segmentation to be evaluated on an instance-basis.
  
  Returns
  -------
  float
      The evaluation results.
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code: def evaluate_instance(
      self,
      model: ModelInterface,
      x: Optional[np.ndarray],
      y: Optional[np.ndarray],
      a: Optional[np.ndarray],
      s: Optional[np.ndarray],
  ) -> float:
      """
      Evaluate instance gets model and data for a single instance as input and returns the evaluation result.
  
      Parameters
      ----------
      model: ModelInterface
          A ModelInteface that is subject to explanation.
      x: np.ndarray
          The input to be evaluated on an instance-basis.
      y: np.ndarray
          The output to be evaluated on an instance-basis.
      a: np.ndarray
          The explanation to be evaluated on an instance-basis.
      s: np.ndarray
          The segmentation to be evaluated on an instance-basis.
  
      Returns
      -------
      float
          The evaluation results.
      """
      # Compute complexity measure.
      return self.complexity_func(
          a=a, x=x, **self.complexity_func_kwargs
      )
  
imports: import sys
  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.functions.complexity_func import discrete_entropy, entropy
  from quantus.functions.normalise_func import normalise_by_average_second_moment_estimate
  from quantus.functions import n_bins_func
  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 3 for calling the EfficientMPRT obj's underlying member method code with example usage: obj.evaluate_instance(model, x, y, a, s) and returns: self