code: def compute_single_metric(
      metric: Scorer,
      prediction: np.ndarray,
      solution: np.ndarray,
      task_type: int,
      X_data: Optional[SUPPORTED_XDATA_TYPES] = None,
  ) -> float:
      """
      Returns a metric for the given Auto-Sklearn Scorer object.
      It's direction is determined by the metric itself.
  
      Parameters
      ----------
      solution: np.ndarray
          The ground truth of the targets
      prediction: np.ndarray
          The best estimate from the model, of the given targets
      task_type: int
          To understand if the problem task is classification
          or regression
      metric: Scorer
          Object that host a function to calculate how good the
          prediction is according to the solution.
      X_data : array-like [n_samples x n_features]
          X data used to obtain the predictions
  
      Returns
      -------
      float
      """
      score = _compute_single_scorer(
          solution=solution,
          prediction=prediction,
          metric=metric,
          X_data=X_data,
          task_type=task_type,
      )
      return metric._sign * score
  
imports: from abc import ABCMeta, abstractmethod
  from typing import Any, Callable, Dict, List, Optional, Sequence, Union
  import collections
  from functools import partial
  from itertools import product
  import numpy
  import sklearn.metrics
  from sklearn.utils.multiclass import type_of_target
  from smac.utils.constants import MAXINT
  from autosklearn.constants import BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, MULTILABEL_CLASSIFICATION, MULTIOUTPUT_REGRESSION, REGRESSION, REGRESSION_TASKS, TASK_TYPES
  from autosklearn.data.target_validator import SUPPORTED_XDATA_TYPES
  from .util import sanitize_array
label_desc: Use image node_id 6 for calling a global function with example usage: compute_single_metric(metric, prediction, solution, task_type, X_data) and returns: unknown