class_docstr: Implementation of Effective complexity metric by Nguyen at el., 2020.
  
  Effective complexity measures how many attributions in absolute values are exceeding a certain threshold (eps)
  where a value above the specified threshold implies that the features are important and under indicates it is not.
  
  References:
      1) An-phi Nguyen and María Rodríguez Martínez.: "On quantitative aspects of model
      interpretability." arXiv preprint arXiv:2007.07584 (2020).
  
  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.
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
  ----------
  a_batch: np.ndarray
      The explanation to be evaluated on a batch-basis.
  kwargs:
      Unused.
  
  Returns
  -------
  scores_batch:
      The evaluation results.
code: def evaluate_batch(self, a_batch: np.ndarray, **kwargs) -> List[int]:
      """
      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
      ----------
      a_batch: np.ndarray
          The explanation to be evaluated on a batch-basis.
      kwargs:
          Unused.
  
      Returns
      -------
      scores_batch:
          The evaluation results.
      """
      return [self.evaluate_instance(a) for a in a_batch]
  
file_path: Quantus/quantus/metrics/complexity/effective_complexity.py
imports: import sys
  from typing import Any, Callable, Dict, List, Optional
  import numpy
  from quantus.helpers import warn
  from quantus.helpers.enums import DataType, EvaluationCategory, ModelType, ScoreDirection
  from quantus.metrics.base import Metric
label_desc: Use image node_id 4 for calling the EffectiveComplexity obj's underlying member method code with example usage: obj.evaluate_batch(a_batch) and returns: unknown