code: def _parse_model_param(cfg, state_dict):
      key_mapping = {
          "extractor_mode": "extractor_mode",
          "conv_feature_layers": "extractor_conv_layer_config",
          "conv_bias": "extractor_conv_bias",
          "encoder_embed_dim": "encoder_embed_dim",
          "dropout_input": "encoder_projection_dropout",
          "conv_pos": "encoder_pos_conv_kernel",
          "conv_pos_groups": "encoder_pos_conv_groups",
          "encoder_layers": "encoder_num_layers",
          "encoder_attention_heads": "encoder_num_heads",
          "attention_dropout": "encoder_attention_dropout",
          "encoder_ffn_embed_dim": "encoder_ff_interm_features",
          "activation_dropout": "encoder_ff_interm_dropout",
          "dropout": "encoder_dropout",
          "layer_norm_first": "encoder_layer_norm_first",
          "layerdrop": "encoder_layer_drop",
          "encoder_layerdrop": "encoder_layer_drop",
      }
      params = {}
      src_dicts = [cfg["model"]]
      if "w2v_args" in cfg["model"]:
          src_dicts.append(cfg["model"]["w2v_args"]["model"])
  
      for src, tgt in key_mapping.items():
          for model_cfg in src_dicts:
              if src in model_cfg:
                  params[tgt] = model_cfg[src]
                  break
      if params["extractor_mode"] == "default":
          params["extractor_mode"] = "group_norm"
      # the following line is commented out to resolve lint warning; uncomment before running script
      # params["extractor_conv_layer_config"] = eval(params["extractor_conv_layer_config"])
      assert len(params) == 15
      params["aux_num_out"] = (
          state_dict["proj.bias"].numel()
          if "proj.bias" in state_dict
          else None
      )
      return params
  
label_desc: Use image node_id 4 for calling a global function with example usage: _parse_model_param(cfg, state_dict) and returns: params