code: def _reduce(
      self, net: Tensor, hw: Optional[Tuple[int, int]] = None
  ) -> Tensor:
      if self.reduction is None:
          return net
      if hw is None:
          msg = "`hw` should be provided when `reduction` is applied"
          raise ValueError(msg)
      net = net.transpose(1, 2).contiguous()
      net = net.view(-1, net.shape[1], *hw)
      net = self.reduction(net)
      return net
  
imports: import math
  import torch
  import numpy
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from typing import Any
  from typing import Dict
  from typing import Type
  from typing import Tuple
  from typing import Optional
  from typing import NamedTuple
  from functools import partial
  from torch.nn import Module
  from cftool.misc import safe_execute
  from cftool.misc import WithRegister
  from .convs import conv_nd
  from .convs import Conv2d
  from .utils import zero_module
  from .common import Lambda
  from .hooks import IAttentionHook
  from .hijacks import IAttention
  from .hijacks import IHijackMixin
  from .hijacks import HijackConv2d
  from .hijacks import HijackLinear
  from .hijacks import HijackCustomLinear
  from .activations import Activation
  from ...misc.toolkit import sdp_attn
  from ...misc.toolkit import gradient_checkpoint
label_desc: Use image node_id 5 for calling the Attention obj's underlying member method code with example usage: obj._reduce(net, hw) and returns: net, net