class_docstr: This class is a tiny modification of CategoricalAccuracy to handle the issue when we desire a single output but
  the network outputs multiple y_pred (e.g. inception)
class_docstr_tok: ["This","class","is","a","tiny","modification","of","CategoricalAccuracy","to","handle","the","issue","when","we","desire","a","single","output","but","the","network","outputs","multiple","y_pred","(","e.g",".","inception",")"]
code: def __call__(self, inputs, y_pred, y_true, is_val=False):
      if is_iterable(y_pred):
          return super().__call__(
              inputs, y_pred[0], y_true, is_val=False
          )
      else:
          return super().__call__(inputs, y_pred, y_true, is_val=False)
  
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 CategoricalAccuracySingleInput obj's underlying member method code with example usage: obj.__call__(inputs, y_pred, y_true, is_val) and returns: super, super