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 forward(self, logits, labels):
      loss_list = []
      for i, loss in enumerate(self.losses):
          output = loss(logits, labels)
          loss_list.append(output * self.coef[i])
      return loss_list
  
file_path: PaddleSeg/paddleseg/models/losses/mixed_loss.py
imports: import numpy
  import paddle
  from paddle import nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
label_desc: Use image node_id 2 for calling the MixedLoss obj's underlying member method code with example usage: obj.forward(logits, labels) and returns: loss_list