docstr: :param y_pred: Predictions from doing the forward pass
  :param y_true: Ground Truth
  :param is_val: Whether this is a validation pass (otherwise assumed training pass)
  
  :return:
code: def __call__(self, inputs, y_pred, y_true, is_val):
      """
  
      :param y_pred: Predictions from doing the forward pass
      :param y_true: Ground Truth
      :param is_val: Whether this is a validation pass (otherwise assumed training pass)
  
      :return:
      """
      raise NotImplementedError(
          "Custom Metrics must implement this function"
      )
  
imports: import torch
  from .utils import th_matrixcorr
  from .meters.averagemeter import AverageMeter
  from .callbacks import Callback
  from .losses import lovaszloss, hingeloss, dice_coeff
label_desc: Use image node_id 1 for calling the Metric obj's underlying member method code with example usage: obj.__call__(inputs, y_pred, y_true, is_val) without return types