          "https://arxiv.org/abs/1707.05776"),
      logcosh=(
          "log(cosh(x)) acts similar to MSE for small errors and to MAE for large errors. Like "
          "MSE, it is very stable and prevents overshoots when errors are near zero. Like MAE, it "
          "is robust to outliers. NB: Due to a bug in PlaidML, this loss does not work on AMD "
          "cards."),
      mae=(
          "Mean absolute error will guide reconstructions of each pixel towards its median value in "
          "the training dataset. Robust to outliers but as a median, it can potentially ignore some "
          "infrequent image types in the dataset."),
      mse=(
 
</s> remove             if isinstance(func, DSSIMObjective):
                # Extract Image Patches in SSIM requires that y_pred be of a known shape, so
                # specifically reshape the tensor.
                n_pred = K.reshape(n_pred, K.int_shape(y_pred))
 </s> add             # Some loss functions requires that y_pred be of a known shape, so specifically
            # reshape the tensor.
            n_pred = K.reshape(n_pred, K.int_shape(y_pred)) </s> remove         if hasattr(loss_func, "__name__") and loss_func.__name__ == "logcosh":
            pytest.skip("LogCosh Loss is not currently compatible with PlaidML")
 </s> add 
        "is robust to outliers."),
          "https://arxiv.org/abs/1707.05776"),
      logcosh=(
          "log(cosh(x)) acts similar to MSE for small errors and to MAE for large errors. Like "
          "MSE, it is very stable and prevents overshoots when errors are near zero. Like MAE, it "
        "is robust to outliers."),
        "is robust to outliers."),
      mae=(
          "Mean absolute error will guide reconstructions of each pixel towards its median value in "
          "the training dataset. Robust to outliers but as a median, it can potentially ignore some "
          "infrequent image types in the dataset."),
      mse=(
keep keep keep keep replace replace keep keep keep keep keep
 Enable LogCosh Loss for AMD 
plugins/train/_config.py