code: def calculate_loss(
      solution: np.ndarray,
      prediction: np.ndarray,
      task_type: int,
      metric: Scorer,
      X_data: Optional[SUPPORTED_XDATA_TYPES] = None,
  ) -> float:
      """Calculate the loss with a given metric
  
      Parameters
      ----------
      solution: np.ndarray
          The solutions
  
      prediction: np.ndarray
          The predictions generated
  
      task_type: int
          The task type of the problem
  
      metric: Scorer
          The metric to use
  
      X_data: Optional[SUPPORTED_XDATA_TYPES]
          X data used to obtain the predictions
      """
      losses = calculate_losses(
          solution=solution,
          prediction=prediction,
          task_type=task_type,
          metrics=[metric],
          X_data=X_data,
      )
      return losses[metric.name]
  
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 4 for calling a global function with example usage: calculate_loss(solution, prediction, task_type, metric, X_data) and returns: losses