class_docstr: Average pooling in the channel dimension
class_docstr_tok: ["Average","pooling","in","the","channel","dimension"]
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      return x.mean(dim=1, keepdim=True)
  
imports: from collections import OrderedDict
  from typing import Optional, Tuple, Type
  import torch
  import torch.nn
  from torchvision.models.resnet import Bottleneck, conv1x1, conv3x3
  from torchvision.ops.misc import FrozenBatchNorm2d
label_desc: Use image node_id 2 for calling the ChannelPool obj's underlying member method code with example usage: obj.forward(x) and returns: x