code: def get_intermediate_features(
      self, x, names: List[str]
  ) -> List[torch.Tensor]:
      trunk_out = self.model.forward_features(x)
      head_out = self.model.forward_head(trunk_out, pre_logits=True)
  
      trunk_pool = F.adaptive_avg_pool2d(trunk_out, output_size=(1, 1))
      trunk_pool = torch.flatten(trunk_pool, start_dim=1)
      trunk_pool_norm = F.normalize(trunk_pool, dim=-1)
      head_out_norm = F.normalize(head_out, dim=-1)
  
      outputs = []
      for feat_name in names:
          if feat_name == "flatten":
              outputs.append(head_out)
          elif feat_name == "flatten_norm":
              outputs.append(head_out_norm)
          elif feat_name == "trunk_pool":
              outputs.append(trunk_pool)
          elif feat_name == "trunk_pool_norm":
              outputs.append(trunk_pool_norm)
          elif feat_name == "trunk":
              outputs.append(trunk_out)
      return outputs
  
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 4 for calling the MobileNetV3_Timm obj's underlying member method code with example usage: obj.get_intermediate_features(x, names) and returns: outputs