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: Accumulate the predictions, ground truths and sample weights if any.
  
  Args:
      y_pred (torch.Tensor): A tensor of predictions of the shape expected by
          the metric functions passed to the class.
      y_true (Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]):
          Ground truths. A tensor of ground truths of the shape expected by
          the metric functions passed to the class.
          It can also be a tuple with two tensors, the first being the
          ground truths and the second corresponding the ``sample_weight``
          argument passed to the metric functions in Scikit-Learn.
docstr_tok: ["Accumulate","the","predictions",",","ground","truths","and","sample","weights","if","any",".","Args",":","y_pred","(","torch.Tensor",")",":","A","tensor","of","predictions","of","the","shape","expected","by","the","metric","functions","passed","to","the","class",".","y_true","(","Union","[","torch.Tensor",",","Tuple","[","torch.Tensor",",","torch.Tensor","]","]",")",":","Ground","truths",".","A","tensor","of","ground","truths","of","the","shape","expected","by","the","metric","functions","passed","to","the","class",".","It","can","also","be","a","tuple","with","two","tensors",",","the","first","being","the","ground","truths","and","the","second","corresponding","the","``","sample_weight","``","argument","passed","to","the","metric","functions","in","Scikit-Learn","."]
code: def update(
      self,
      y_pred: torch.Tensor,
      y_true: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]],
  ) -> None:
      """
      Accumulate the predictions, ground truths and sample weights if any.
  
      Args:
          y_pred (torch.Tensor): A tensor of predictions of the shape expected by
              the metric functions passed to the class.
          y_true (Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]):
              Ground truths. A tensor of ground truths of the shape expected by
              the metric functions passed to the class.
              It can also be a tuple with two tensors, the first being the
              ground truths and the second corresponding the ``sample_weight``
              argument passed to the metric functions in Scikit-Learn.
      """
      self._update(y_pred, y_true)
  
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 5 for calling the SKLearnMetrics obj's underlying member method code with example usage: obj.update(y_pred, y_true) without return types