code: def get_name_convert_func():
      """
      Get the function to convert Caffe2 layer names to PyTorch layer names.
      Returns:
          (func): function to convert parameter name from Caffe2 format to PyTorch
          format.
      """
      pairs = [
          # ------------------------------------------------------------
          # 'nonlocal_conv3_1_theta_w' -> 's3.pathway0_nonlocal3.conv_g.weight'
          [
              r"^nonlocal_conv([0-9]+)_([0-9]+)_(.*)",
              r"s\1.pathway0_nonlocal\2_\3",
          ],
          # 'theta' -> 'conv_theta'
          [
              r"^(.*)_nonlocal([0-9]+)_(theta)(.*)",
              r"\1_nonlocal\2.conv_\3\4",
          ],
          # 'g' -> 'conv_g'
          [
              r"^(.*)_nonlocal([0-9]+)_(g)(.*)",
              r"\1_nonlocal\2.conv_\3\4",
          ],
          # 'phi' -> 'conv_phi'
          [
              r"^(.*)_nonlocal([0-9]+)_(phi)(.*)",
              r"\1_nonlocal\2.conv_\3\4",
          ],
          # 'out' -> 'conv_out'
          [
              r"^(.*)_nonlocal([0-9]+)_(out)(.*)",
              r"\1_nonlocal\2.conv_\3\4",
          ],
          # 'nonlocal_conv4_5_bn_s' -> 's4.pathway0_nonlocal3.bn.weight'
          [r"^(.*)_nonlocal([0-9]+)_(bn)_(.*)", r"\1_nonlocal\2.\3.\4"],
          # ------------------------------------------------------------
          # 't_pool1_subsample_bn' -> 's1_fuse.conv_f2s.bn.running_mean'
          [r"^t_pool1_subsample_bn_(.*)", r"s1_fuse.bn.\1"],
          # 't_pool1_subsample' -> 's1_fuse.conv_f2s'
          [r"^t_pool1_subsample_(.*)", r"s1_fuse.conv_f2s.\1"],
          # 't_res4_5_branch2c_bn_subsample_bn_rm' -> 's4_fuse.conv_f2s.bias'
          [
              r"^t_res([0-9]+)_([0-9]+)_branch2c_bn_subsample_bn_(.*)",
              r"s\1_fuse.bn.\3",
          ],
          # 't_pool1_subsample' -> 's1_fuse.conv_f2s'
          [
              r"^t_res([0-9]+)_([0-9]+)_branch2c_bn_subsample_(.*)",
              r"s\1_fuse.conv_f2s.\3",
          ],
          # ------------------------------------------------------------
          # 'res4_4_branch_2c_bn_b' -> 's4.pathway0_res4.branch2.c_bn_b'
          [
              r"^res([0-9]+)_([0-9]+)_branch([0-9]+)([a-z])_(.*)",
              r"s\1.pathway0_res\2.branch\3.\4_\5",
          ],
          # 'res_conv1_bn_' -> 's1.pathway0_stem.bn.'
          [r"^res_conv1_bn_(.*)", r"s1.pathway0_stem.bn.\1"],
          # 'conv1_xy_w_momentum' -> 's1.pathway0_stem.conv_xy.'
          [r"^conv1_xy(.*)", r"s1.pathway0_stem.conv_xy\1"],
          # 'conv1_w_momentum' -> 's1.pathway0_stem.conv.'
          [r"^conv1_(.*)", r"s1.pathway0_stem.conv.\1"],
          # 'res4_0_branch1_w' -> 'S4.pathway0_res0.branch1.weight'
          [
              r"^res([0-9]+)_([0-9]+)_branch([0-9]+)_(.*)",
              r"s\1.pathway0_res\2.branch\3_\4",
          ],
          # 'res_conv1_' -> 's1.pathway0_stem.conv.'
          [r"^res_conv1_(.*)", r"s1.pathway0_stem.conv.\1"],
          # ------------------------------------------------------------
          # 'res4_4_branch_2c_bn_b' -> 's4.pathway0_res4.branch2.c_bn_b'
          [
              r"^t_res([0-9]+)_([0-9]+)_branch([0-9]+)([a-z])_(.*)",
              r"s\1.pathway1_res\2.branch\3.\4_\5",
          ],
          # 'res_conv1_bn_' -> 's1.pathway0_stem.bn.'
          [r"^t_res_conv1_bn_(.*)", r"s1.pathway1_stem.bn.\1"],
          # 'conv1_w_momentum' -> 's1.pathway0_stem.conv.'
          [r"^t_conv1_(.*)", r"s1.pathway1_stem.conv.\1"],
          # 'res4_0_branch1_w' -> 'S4.pathway0_res0.branch1.weight'
          [
              r"^t_res([0-9]+)_([0-9]+)_branch([0-9]+)_(.*)",
              r"s\1.pathway1_res\2.branch\3_\4",
          ],
          # 'res_conv1_' -> 's1.pathway0_stem.conv.'
          [r"^t_res_conv1_(.*)", r"s1.pathway1_stem.conv.\1"],
          # ------------------------------------------------------------
          # pred_ -> head.projection.
          [r"pred_(.*)", r"head.projection.\1"],
          # '.b_bn_fc' -> '.se.fc'
          [r"(.*)b_bn_fc(.*)", r"\1se.fc\2"],
          # conv_5 -> head.conv_5.
          [r"conv_5(.*)", r"head.conv_5\1"],
          # conv_5 -> head.conv_5.
          [r"lin_5(.*)", r"head.lin_5\1"],
          # '.bn_b' -> '.weight'
          [r"(.*)bn.b\Z", r"\1bn.bias"],
          # '.bn_s' -> '.weight'
          [r"(.*)bn.s\Z", r"\1bn.weight"],
          # '_bn_rm' -> '.running_mean'
          [r"(.*)bn.rm\Z", r"\1bn.running_mean"],
          # '_bn_riv' -> '.running_var'
          [r"(.*)bn.riv\Z", r"\1bn.running_var"],
          # '_b' -> '.bias'
          [r"(.*)[\._]b\Z", r"\1.bias"],
          # '_w' -> '.weight'
          [r"(.*)[\._]w\Z", r"\1.weight"],
      ]
  
      def convert_caffe2_name_to_pytorch(caffe2_layer_name):
          """
          Convert the caffe2_layer_name to pytorch format by apply the list of
          regular expressions.
          Args:
              caffe2_layer_name (str): caffe2 layer name.
          Returns:
              (str): pytorch layer name.
          """
          for source, dest in pairs:
              caffe2_layer_name = re.sub(
                  source, dest, caffe2_layer_name
              )
          return caffe2_layer_name
  
      return convert_caffe2_name_to_pytorch
  
label_desc: Use image node_id 1 for calling a global function with example usage: get_name_convert_func() and returns: convert_caffe2_name_to_pytorch, caffe2_layer_name