code: def get_trunk_output_feature_names(model_config):
      """
      Get the feature names which we will use to associate the features witl.
      If Feature eval mode is set, we get feature names from
      config.FEATURE_EVAL_SETTINGS.LINEAR_EVAL_FEAT_POOL_OPS_MAP.
      """
      feature_names = []
      if is_feature_extractor_model(model_config):
          feat_ops_map = (
              model_config.FEATURE_EVAL_SETTINGS.LINEAR_EVAL_FEAT_POOL_OPS_MAP
          )
          feature_names = [item[0] for item in feat_ops_map]
      return feature_names
  
imports: import logging
  import math
  import warnings
  from enum import Enum
  from typing import Dict, List, Tuple
  import torch
  import torch.distributed
  import torch.nn
  import torch.nn.functional
  from torch.nn.modules.utils import _ntuple
  from torch.utils.checkpoint import checkpoint
  from vissl.data.collators.collator_helper import MultiDimensionalTensor
  from vissl.utils.activation_checkpointing import checkpoint_trunk
  from vissl.utils.env import get_machine_local_and_dist_rank
  from vissl.utils.misc import is_apex_available
label_desc: Use image node_id 4 for calling a global function with example usage: get_trunk_output_feature_names(model_config) and returns: feature_names