class_docstr: Balanced CrossEntropy Loss on `binary`,
  MaskL1Loss on `thresh`,
  DiceLoss on `thresh_binary`.
  Note: The meaning of inputs can be figured out in `SegDetectorLossBuilder`.
code: def __init__(self, eps=1e-6, l1_scale=10, bce_scale=5):
      super(L1BalanceCELoss, self).__init__()
      from .dice_loss import DiceLoss
      from .l1_loss import MaskL1Loss
      from .balance_cross_entropy_loss import BalanceCrossEntropyLoss
  
      self.dice_loss = DiceLoss(eps=eps)
      self.l1_loss = MaskL1Loss()
      self.bce_loss = BalanceCrossEntropyLoss()
  
      self.l1_scale = l1_scale
      self.bce_scale = bce_scale
  
imports: import sys
  import torch
  import torch.nn
label_desc: Use image node_id 1 to create a new L1BalanceCELoss object from inherited base classes: nn with example: obj = L1BalanceCELoss(eps, l1_scale, bce_scale)