code: def init_conv_with_tf_weights(conv, weights, bias=None):
      # Shifting input and output channel dimensions.
      weights = weights.transpose((3, 2, 0, 1))
      if conv.W.shape != weights.shape:  # for depthwise conv
          weights = weights.transpose((1, 0, 2, 3))
      conv.W.data[:] = weights.data[:]
  
      if bias is not None:
          conv.b.data[:] = bias.data[:]
  
file_path: chainercv/examples/mobilenet/tfckpt2npz.py
imports: import argparse
  import chainer
  import tensorflow
  from chainercv.links import MobileNetV2
label_desc: Use image node_id 2 for calling a global function with example usage: init_conv_with_tf_weights(conv, weights, bias) without return types