code: def copy_conv2d_bn_activ(layer, config, cba, inverse_ch=False):
      if "Convolution" in layer.type:
          cba.conv = copy_conv(
              layer, config, cba.conv, inverse_ch=inverse_ch
          )
      elif "BN" in layer.type:
          cba.bn.eps = config.bn_param.eps
          cba.bn.decay = config.bn_param.momentum
          cba.bn.gamma.data.ravel()[:] = np.array(layer.blobs[0].data)
          cba.bn.beta.data.ravel()[:] = np.array(layer.blobs[1].data)
          cba.bn.avg_mean.ravel()[:] = np.array(layer.blobs[2].data)
          cba.bn.avg_var.ravel()[:] = np.array(layer.blobs[3].data)
      else:
          print("Ignored: {} ({})".format(layer.name, layer.type))
      return cba
  
file_path: chainercv/examples/pspnet/caffe2npz.py
imports: import argparse
  from google.protobuf import text_format
  import numpy
  import re
  from chainer import serializers
  from chainercv.experimental.links import PSPNetResNet101
  import caffe_pb2
label_desc: Use image node_id 2 for calling a global function with example usage: copy_conv2d_bn_activ(layer, config, cba, inverse_ch) and returns: cba