class_docstr: Weighted computations for multiple Loss.
  The advantage is that mixed loss training can be achieved without changing the networking code.
  
  Args:
      losses (list[nn.Layer]): A list consisting of multiple loss classes
      coef (list[float|int]): Weighting coefficient of multiple loss
  
  Returns:
      A callable object of MixedLoss.
code: def __init__(self, losses, coef):
      super(MixedLoss, self).__init__()
      if not isinstance(losses, list):
          raise TypeError("`losses` must be a list!")
      if not isinstance(coef, list):
          raise TypeError("`coef` must be a list!")
      len_losses = len(losses)
      len_coef = len(coef)
      if len_losses != len_coef:
          raise ValueError(
              "The length of `losses` should equal to `coef`, but they are {} and {}.".format(
                  len_losses, len_coef
              )
          )
  
      self.losses = losses
      self.coef = coef
  
file_path: PaddleSeg/contrib/MedicalSeg/medicalseg/models/losses/mixes_losses.py
imports: import numpy
  import paddle
  from paddle import nn
  import paddle.nn.functional
  from medicalseg.cvlibs import manager
label_desc: Use image node_id 1 to create a new MixedLoss object from inherited base classes: nn with example: obj = MixedLoss(losses, coef)