class_docstr: This class will be used to perform annealing between two losses
code: def forward(self, loss_1, loss_2, **kwargs):
      annealing_alpha = kwargs.get("annealing_alpha", None)
      if annealing_alpha is None:
          return self._coeff * loss_1 + (1 - self._coeff) * loss_2
      else:
          return (
              1 - annealing_alpha
          ) * loss_1 + annealing_alpha * loss_2
  
imports: from typing import Any
  from collections import OrderedDict
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.autograd import Variable
  from torch_points3d.datasets.segmentation import IGNORE_LABEL
  from .metric_losses import *
label_desc: Use image node_id 2 for calling the LossAnnealer obj's underlying member method code with example usage: obj.forward(loss_1, loss_2) and returns: unknown, unknown