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.
docstr: Returns the metrics as a dictionary with the names as keys.
docstr_tok: ["Returns","the","metrics","as","a","dictionary","with","the","names","as","keys","."]
code: def compute(self) -> Dict:
      """
      Returns the metrics as a dictionary with the names as keys.
      """
      sample_weight = None
      if len(self.sample_weight_list) != 0:
          sample_weight = np.concatenate(self.sample_weight_list)
      y_pred = np.concatenate(self.y_pred_list)
      y_true = np.concatenate(self.y_true_list)
      return self._compute(y_true, y_pred, sample_weight)
  
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 7 for calling the SKLearnMetrics obj's underlying member method code with example usage: obj.compute() and returns: self