code: def __init__(
      self,
      student_channels=256,
      teacher_channels=256,
      normalize=True,
      loss_weight=1.0,
      loss_func="mse",
  ):
      super(MGDFeatureLoss, self).__init__()
      self.normalize = normalize
      self.loss_weight = loss_weight
      assert loss_func in ["mse", "ssim"]
      self.loss_func = loss_func
      self.mse_loss = nn.MSELoss(reduction="sum")
      self.ssim_loss = SSIM(11)
  
      kaiming_init = parameter_init("kaiming")
      if student_channels != teacher_channels:
          self.align = nn.Conv2D(
              student_channels,
              teacher_channels,
              kernel_size=1,
              stride=1,
              padding=0,
              weight_attr=kaiming_init,
              bias_attr=False,
          )
      else:
          self.align = None
  
      self.generation = nn.Sequential(
          nn.Conv2D(
              teacher_channels,
              teacher_channels,
              kernel_size=3,
              padding=1,
          ),
          nn.ReLU(),
          nn.Conv2D(
              teacher_channels,
              teacher_channels,
              kernel_size=3,
              padding=1,
          ),
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle import ParamAttr
  from ppdet.core.workspace import register
  from ppdet.modeling import ops
  from ppdet.modeling.losses.iou_loss import GIoULoss
  from ppdet.utils.logger import setup_logger
label_desc: Use image node_id 1 to create a new MGDFeatureLoss object from inherited base classes: nn with example: obj = MGDFeatureLoss(student_channels, teacher_channels, normalize, loss_weight, loss_func)