class_docstr: Overall loss function of the DRAEM model.
  
  The total loss consists of the sum of the L2 loss and Focal loss between the reconstructed image and the input
  image, and the Structural Similarity loss between the predicted and GT anomaly masks.
class_docstr_tok: ["Overall","loss","function","of","the","DRAEM","model",".","The","total","loss","consists","of","the","sum","of","the","L2","loss","and","Focal","loss","between","the","reconstructed","image","and","the","input","image",",","and","the","Structural","Similarity","loss","between","the","predicted","and","GT","anomaly","masks","."]
code: def __init__(self) -> None:
      super().__init__()
  
      self.l2_loss = nn.modules.loss.MSELoss()
      self.focal_loss = FocalLoss(alpha=1, reduction="mean")
      self.ssim_loss = SSIMLoss(window_size=11)
  
imports: from kornia.losses import FocalLoss, SSIMLoss
  from torch import Tensor, nn
label_desc: Use image node_id 1 to create a new DraemLoss object from inherited base classes: nn with example: obj = DraemLoss()