class_docstr: The abstract class representing a metric which can be accumulated at each batch and calculated at the end
  of the epoch.
docstr: Update the current state of the metric and return the metric for the current batch. This method has to
  be implemented if the metric is used as a **batch metric**. If used as an epoch metric, it does not need to be
  implemented.
  
  Args:
      y_pred: The prediction of the model.
      y_true: Target to evaluate the model.
  
  Returns:
      The value of the metric for the current batch.
code: def forward(self, y_pred, y_true):
      """
      Update the current state of the metric and return the metric for the current batch. This method has to
      be implemented if the metric is used as a **batch metric**. If used as an epoch metric, it does not need to be
      implemented.
  
      Args:
          y_pred: The prediction of the model.
          y_true: Target to evaluate the model.
  
      Returns:
          The value of the metric for the current batch.
      """
      raise NotImplementedError
  
file_path: poutyne/poutyne/framework/metrics/base.py
imports: from abc import ABC, abstractmethod
  import torch.nn
label_desc: Use image node_id 1 for calling the Metric obj's underlying member method code with example usage: obj.forward(y_pred, y_true) without return types