code: def convert_heads_to_classy_model(
      state_dict,
      out_prefix,
      num_fc_layers,
      use_bn_head=False,
      use_bias_head_fc=True,
  ):
      """
      Convert an MLP head of 2 kinds:
      Type 1: FC only
      Type 2: FC -> RELU -> FC
      """
      logger.info("Converting head...")
      converted_dict = {"block3-2": {"default_head": {}}}
      if num_fc_layers > 1:
          out_dict = {}
          for idx in range(num_fc_layers - 1):
              # convert the linear layer
              local_prefix = f"mlp.layers.{idx}"
              out_dict[f"{local_prefix}.fc.weight"] = state_dict[
                  f"{idx}.clf.0.weight"
              ]
              if use_bias_head_fc:
                  # linear layer bias can be optional
                  out_dict[f"{local_prefix}.fc.bias"] = state_dict[
                      f"{idx}.clf.0.bias"
                  ]
  
              # convert BN if we have BN in the head
              if use_bn_head:
                  bn = f"{local_prefix}.batch_norm"
                  out_dict[f"{bn}.weight"] = state_dict[
                      f"{idx}.clf.1.weight"
                  ]
                  out_dict[f"{bn}.bias"] = state_dict[
                      f"{idx}.clf.1.bias"
                  ]
                  out_dict[f"{bn}.running_mean"] = state_dict[
                      f"{idx}.clf.1.running_mean"
                  ]
                  out_dict[f"{bn}.running_var"] = state_dict[
                      f"{idx}.clf.1.running_var"
                  ]
  
          # for the final output layer, the name is embedding_fc.weight/bias
          out_dict[f"{out_prefix}_fc.weight"] = state_dict[
              f"{idx + 1}.clf.0.weight"
          ]
          if use_bias_head_fc:
              out_dict[f"{out_prefix}_fc.bias"] = state_dict[
                  f"{idx + 1}.clf.0.bias"
              ]
          converted_dict["block3-2"]["default_head"] = out_dict
      else:
          # for converting NPID models, there's only one projection FC
          converted_dict = {
              "block3-2": {
                  "default_head": {
                      f"{out_prefix}_fc.weight": state_dict[
                          "0.clf.0.weight"
                      ],
                      f"{out_prefix}_fc.bias": state_dict[
                          "0.clf.0.bias"
                      ],
                  }
              }
          }
      return converted_dict
  
imports: import argparse
  import logging
  import sys
  import torch
label_desc: Use image node_id 2 for calling a global function with example usage: convert_heads_to_classy_model(state_dict, out_prefix, num_fc_layers, use_bn_head, use_bias_head_fc) and returns: converted_dict