code: def forward_features(self, x):
      x = self.model.forward_features(x)
      if self.trunk_only:
          x = F.adaptive_avg_pool2d(x, output_size=(1, 1))
          x = x.flatten(start_dim=1)
          return [x]
  
      x = self.model.forward_head(x, pre_logits=True)
      return [x]
  
imports: from functools import partial
  from typing import List
  import torch
  import torch.nn
  import torch.nn.functional
  from timm.models.mobilenetv3 import mobilenetv3_large_100
  from torchvision.models import mobilenet_v3_large
  from vissl.config import AttrDict
  from vissl.models.trunks import register_model_trunk
label_desc: Use image node_id 3 for calling the MobileNetV3_Timm obj's underlying member method code with example usage: obj.forward_features(x) and returns: list, list