class_docstr: Implementation of Focus evaluation strategy by Arias et. al. 2022
  
  The Focus is computed through mosaics of instances from different classes, and the explanations these generate.
  Each mosaic contains four images: two images belonging to the target class (the specific class the feature
  attribution method is expected to explain) and the other two are chosen randomly from the rest of classes.
  Thus, the Focus estimates the reliability of feature attribution method’s output as the probability of the sampled
  pixels lying on an image of the target class of the mosaic. This is equivalent to the proportion
  of positive relevance lying on those images.
  
  References:
      1) Anna Arias-Duart et al.: "Focus! Rating XAI Methods
      and Finding Biases" FUZZ-IEEE (2022): 1-8.
  
  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 quadrant_bottom_left(self, a: np.ndarray) -> np.ndarray:
      quandrant_a = a[
          :,
          : int(self.mosaic_shape[1] / 2),
          int(self.mosaic_shape[2] / 2) :,
      ]
      return quandrant_a
  
file_path: Quantus/quantus/metrics/localisation/focus.py
imports: import sys
  from typing import Any, Callable, Dict, List, Optional
  import numpy
  from quantus.helpers import plotting, 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 7 for calling the Focus obj's underlying member method code with example usage: obj.quadrant_bottom_left(a) and returns: quandrant_a