docstr: Sets the module in training mode.
  
  This has any effect only on certain modules. See documentations of
  particular modules for details of their behaviors in training/evaluation
  mode, if they are affected, e.g. :class:`Dropout`, :class:`BatchNorm`,
  etc.
  
  Returns:
      Module: self
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code: def train_fixbn(
      self, mode=True, freeze_bn=True, freeze_bn_affine=False
  ):
      r"""Sets the module in training mode.
  
      This has any effect only on certain modules. See documentations of
      particular modules for details of their behaviors in training/evaluation
      mode, if they are affected, e.g. :class:`Dropout`, :class:`BatchNorm`,
      etc.
  
      Returns:
          Module: self
      """
      super(DeepLabv3_plus, self).train(mode)
      if freeze_bn:
          print("Freezing Mean/Var of BatchNorm2D.")
          if freeze_bn_affine:
              print("Freezing Weight/Bias of BatchNorm2D.")
      if freeze_bn:
          for m in self.xception_features.modules():
              if isinstance(m, nn.BatchNorm2d):
                  m.eval()
                  if freeze_bn_affine:
                      m.weight.requires_grad = False
                      m.bias.requires_grad = False
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  from collections import OrderedDict
  import torch.utils.model_zoo
  from torch.nn.parameter import Parameter
label_desc: Use image node_id 8 for calling the DeepLabv3_plus obj's underlying member method code with example usage: obj.train_fixbn(mode, freeze_bn, freeze_bn_affine) without return types