class_docstr: Implementation of the Random Logit Metric by Sixt et al., 2020.
  
  The Random Logit Metric computes the distance between the original explanation and a reference explanation of
  a randomly chosen non-target class.
  
  References:
      1) Leon Sixt et al.: "When Explanations Lie: Why Many Modified BP
      Attributions Fail." ICML (2020): 9046-9057.
  
  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: This method performs XAI evaluation on a single batch of explanations.
  For more information on the specific logic, we refer the metric’s initialisation docstring.
  
  Parameters
  ----------
  model:
      A model that is subject to explanation.
  x_batch:
      A np.ndarray which contains the input data that are explained.
  y_batch:
      A np.ndarray which contains the output labels that are explained.
  a_batch:
      A np.ndarray which contains pre-computed attributions i.e., explanations.
  kwargs:
      Unused.
  
  Returns
  -------
  scores_batch:
      Evaluation results.
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code: def evaluate_batch(
      self,
      model: ModelInterface,
      x_batch: np.ndarray,
      y_batch: np.ndarray,
      a_batch: np.ndarray,
      **kwargs,
  ) -> List[float]:
      """
      This method performs XAI evaluation on a single batch of explanations.
      For more information on the specific logic, we refer the metric’s initialisation docstring.
  
      Parameters
      ----------
      model:
          A model that is subject to explanation.
      x_batch:
          A np.ndarray which contains the input data that are explained.
      y_batch:
          A np.ndarray which contains the output labels that are explained.
      a_batch:
          A np.ndarray which contains pre-computed attributions i.e., explanations.
      kwargs:
          Unused.
  
      Returns
      -------
      scores_batch:
          Evaluation results.
      """
      return [
          self.evaluate_instance(model, x, y, a)
          for x, y, a in zip(x_batch, y_batch, a_batch)
      ]
  
file_path: Quantus/quantus/metrics/randomisation/random_logit.py
imports: import sys
  from typing import Any, Callable, Dict, List, Optional
  import numpy
  from quantus.functions.similarity_func import ssim
  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 5 for calling the RandomLogit obj's underlying member method code with example usage: obj.evaluate_batch(model, x_batch, y_batch, a_batch) and returns: unknown