code: def __init__(
      self,
      student_channels=256,
      teacher_channels=256,
      normalize=True,
      loss_weight=1.0,
  ):
      super(MimicFeatureLoss, self).__init__()
      self.normalize = normalize
      self.loss_weight = loss_weight
      self.mse_loss = nn.MSELoss()
  
      if student_channels != teacher_channels:
          self.align = nn.Conv2D(
              student_channels,
              teacher_channels,
              kernel_size=1,
              stride=1,
              padding=0,
          )
      else:
          self.align = None
  
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 MimicFeatureLoss object from inherited base classes: nn with example: obj = MimicFeatureLoss(student_channels, teacher_channels, normalize, loss_weight)