code: def feature_norm(feat):
      # Normalize the feature maps to have zero mean and unit variances.
      assert len(feat.shape) == 4
      N, C, H, W = feat.shape
      feat = feat.transpose([1, 0, 2, 3]).reshape([C, -1])
      mean = feat.mean(axis=-1, keepdim=True)
      std = feat.std(axis=-1, keepdim=True)
      feat = (feat - mean) / (std + 1e-6)
      return feat.reshape([C, N, H, W]).transpose([1, 0, 2, 3])
  
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 2 for calling a global function with example usage: feature_norm(feat) and returns: feat