class_docstr: Focal and Global Knowledge Distillation for Detectors
  The code is reference from https://github.com/yzd-v/FGD/blob/master/mmdet/distillation/losses/fgd.py
  
  Args:
      student_channels (int): The number of channels in the student's FPN feature map. Default to 256.
      teacher_channels (int): The number of channels in the teacher's FPN feature map. Default to 256.
      normalize (bool): Whether to normalize the feature maps.
      temp (float, optional): The temperature coefficient. Defaults to 0.5.
      alpha_fgd (float, optional): The weight of fg_loss. Defaults to 0.001
      beta_fgd (float, optional): The weight of bg_loss. Defaults to 0.0005
      gamma_fgd (float, optional): The weight of mask_loss. Defaults to 0.001
      lambda_fgd (float, optional): The weight of relation_loss. Defaults to 0.000005
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code: def spatial_pool(self, x, mode="teacher"):
      batch, channel, width, height = x.shape
      x_copy = x
      x_copy = paddle.reshape(x_copy, [batch, channel, height * width])
      x_copy = x_copy.unsqueeze(1)
      if mode.lower() == "student":
          context_mask = self.conv_mask_s(x)
      else:
          context_mask = self.conv_mask_t(x)
  
      context_mask = paddle.reshape(
          context_mask, [batch, 1, height * width]
      )
      context_mask = F.softmax(context_mask, axis=2)
      context_mask = context_mask.unsqueeze(-1)
      context = paddle.matmul(x_copy, context_mask)
      context = paddle.reshape(context, [batch, channel, 1, 1])
      return context
  
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 3 for calling the FGDFeatureLoss obj's underlying member method code with example usage: obj.spatial_pool(x, mode) and returns: context