code: def is_feature_extractor_model(model_config):
      """
      If the model is a feature extractor model:
          - evaluation model is on
          - trunk is frozen
          - number of features specified for features extraction > 0
      """
      return (
          model_config.FEATURE_EVAL_SETTINGS.EVAL_MODE_ON
          and (
              model_config.FEATURE_EVAL_SETTINGS.FREEZE_TRUNK_ONLY
              or model_config.FEATURE_EVAL_SETTINGS.FREEZE_TRUNK_AND_HEAD
          )
          and len(
              model_config.FEATURE_EVAL_SETTINGS.LINEAR_EVAL_FEAT_POOL_OPS_MAP
          )
          > 0
      )
  
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 3 for calling a global function with example usage: is_feature_extractor_model(model_config) and returns: unknown