docstr: When using the metric as a batch metric, this is called. If the metric is only
  used as an epoch metric, there is no need to implement this.
docstr_tok: ["When","using","the","metric","as","a","batch","metric",",","this","is","called",".","If","the","metric","is","only","used","as","an","epoch","metric",",","there","is","no","need","to","implement","this","."]
code: def forward(self, y_pred, y_true):
      """
      When using the metric as a batch metric, this is called. If the metric is only
      used as an epoch metric, there is no need to implement this.
      """
      num_true_positives, total_exemples = self._update(y_pred, y_true)
      return self._compute_accuracy_from_stats(
          num_true_positives, total_exemples
      )
  
file_path: poutyne/examples/medium_post/train_with_custom_metrics.py
imports: import torch
  import torch.nn
  from torch.utils.data import random_split
  from torchvision.datasets import MNIST
  from torchvision.transforms import ToTensor
  from poutyne import Model, Metric
label_desc: Use image node_id 5 for calling the MyAccuracyMetric obj's underlying member method code with example usage: obj.forward(y_pred, y_true) and returns: self