class_docstr: Wrap metrics with Scikit-learn-like interface
  (``metric(y_true, y_pred, sample_weight=sample_weight, **kwargs)``).
  The ``SKLearnMetrics`` object has to keep in memory the ground truths and
  predictions so that in can compute the metric at the end.
  
  Example:
      .. code-block:: python
  
          from sklearn.metrics import roc_auc_score, average_precision_score
          from poutyne import SKLearnMetrics
          my_epoch_metric = SKLearnMetrics([roc_auc_score, average_precision_score])
  
  Args:
      funcs (Union[Callable, List[Callable]]): A metric or a list of metrics with a
          scikit-learn-like interface.
      kwargs (Optional[Union[dict, List[dict]]]): Optional dictionary of list of dictionaries
          corresponding to keyword arguments to pass to each corresponding metric.
          (Default value = None)
      names (Optional[Union[str, List[str]]]): Optional string or list of strings corresponding to
          the names given to the metrics. By default, the names are the names of the functions.
code: def _compute(self, y_true, y_pred, sample_weight):
      return {
          name: func(
              y_true, y_pred, sample_weight=sample_weight, **kwargs
          )
          for name, func, kwargs in zip(
              self.__name__, self.funcs, self.kwargs
          )
      }
  
file_path: poutyne/poutyne/framework/metrics/predefined/sklearn_metrics.py
imports: from typing import Callable, Dict, List, Optional, Tuple, Union
  import numpy
  import torch
  from poutyne.framework.metrics.base import Metric
label_desc: Use image node_id 8 for calling the SKLearnMetrics obj's underlying member method code with example usage: obj._compute(y_true, y_pred, sample_weight) and returns: unknown