class_docstr: Applies a max_pool2d (not actual max_pool2d, we just subsample) on top of the last feature map
code: def forward(
      self,
      x: List[Tensor],
      y: List[Tensor],
      names: List[str],
  ) -> Tuple[List[Tensor], List[str]]:
      names.append("pool")
      # Use max pooling to simulate stride 2 subsampling
      x.append(F.max_pool2d(x[-1], kernel_size=1, stride=2, padding=0))
      return x, names
  
imports: from collections import OrderedDict
  from typing import Callable, Dict, List, Optional, Tuple
  import torch.nn.functional
  from torch import nn, Tensor
  from ..ops.misc import Conv2dNormActivation
  from ..utils import _log_api_usage_once
label_desc: Use image node_id 1 for calling the LastLevelMaxPool obj's underlying member method code with example usage: obj.forward(x, y, names) and returns: x,  names