code: def convert_trunk_to_classy_model(state_dict_trunk, depth):
      assert (
          depth in BLOCK_CONFIG.keys()
      ), f"depth {depth} conversion not supported"
      layers = BLOCK_CONFIG[depth]
  
      output_dict = {}
      for k, val in state_dict_trunk.items():
          if any(x in k for x in _SKIP_LAYERS_IN_TRUNK):
              continue
          k = k.replace(
              "_feature_blocks.conv1.", "initial_block._module.0."
          )
          k = k.replace(
              "_feature_blocks.bn1.", "initial_block._module.1."
          )
          for idx in range(len(layers)):
              num_blocks = layers[idx]
              for j in range(num_blocks):
                  k = k.replace(
                      f"_feature_blocks.layer{idx + 1}.{j}.",
                      f"blocks.{idx}.block{idx}-{j}.",
                  )
          k = k.replace(
              ".conv1.weight", ".convolutional_block.0.weight"
          )
          k = k.replace(
              ".conv2.weight", ".convolutional_block.3.weight"
          )
          k = k.replace(
              ".conv3.weight", ".convolutional_block.6.weight"
          )
          k = k.replace(".bn1.", ".convolutional_block.1.")
          k = k.replace(".bn2.", ".convolutional_block.4.")
          k = k.replace(".bn3.", ".bn.")
          output_dict[k] = val
      return output_dict
  
imports: import argparse
  import logging
  import sys
  import torch
label_desc: Use image node_id 1 for calling a global function with example usage: convert_trunk_to_classy_model(state_dict_trunk, depth) and returns: output_dict