class_docstr: Interface defining Metrics' API.
docstr: Evaluates model and attributes on a single data batch and returns the batched evaluation result.
  
  This method needs to be implemented to use __call__().
  
  Parameters
  ----------
  model: ModelInterface
      A ModelInteface that is subject to explanation.
  x_batch: np.ndarray
      The input to be evaluated on a batch-basis.
  y_batch: np.ndarray
      The output to be evaluated on a batch-basis.
  a_batch: np.ndarray
      The explanation to be evaluated on a batch-basis.
  s_batch: np.ndarray
      The segmentation to be evaluated on a batch-basis.
  
  Returns
  -------
  np.ndarray
      The batched 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,
      s_batch: Optional[np.ndarray],
      **kwargs,
  ) -> R:
      """
      Evaluates model and attributes on a single data batch and returns the batched evaluation result.
  
      This method needs to be implemented to use __call__().
  
      Parameters
      ----------
      model: ModelInterface
          A ModelInteface that is subject to explanation.
      x_batch: np.ndarray
          The input to be evaluated on a batch-basis.
      y_batch: np.ndarray
          The output to be evaluated on a batch-basis.
      a_batch: np.ndarray
          The explanation to be evaluated on a batch-basis.
      s_batch: np.ndarray
          The segmentation to be evaluated on a batch-basis.
  
      Returns
      -------
      np.ndarray
          The batched evaluation results.
      """
      raise NotImplementedError()
  
imports: from __future__ import annotations
  import functools
  import logging
  import os
  import sys
  from abc import abstractmethod
  from typing import Any, Callable, ClassVar, Dict, Generator, Generic, Sequence, Set, TypeVar, Optional, Union, TYPE_CHECKING, no_type_check
  import matplotlib.pyplot
  import numpy
  from sklearn.utils import gen_batches
  from tqdm.auto import tqdm
  from quantus.helpers import asserts, utils, warn
  from quantus.functions.normalise_func import normalise_by_max
  from quantus.helpers.enums import DataType, EvaluationCategory, ModelType, ScoreDirection
  from quantus.helpers.model.model_interface import ModelInterface
label_desc: Use image node_id 3 for calling the Metric obj's underlying member method code with example usage: obj.evaluate_batch(model, x_batch, y_batch, a_batch, s_batch) without return types