code: def forward(self, x: torch.Tensor):
      identity = x
  
      out = self.relu1(self.bn1(self.conv1(x)))
      out = self.relu2(self.bn2(self.conv2(out)))
      out = self.avgpool(out)
      out = self.bn3(self.conv3(out))
  
      if self.downsample is not None:
          identity = self.downsample(x)
  
      out += identity
      out = self.relu3(out)
      return out
  
imports: from collections import OrderedDict
  from typing import Tuple, Union
  import numpy
  import torch
  import torch.nn.functional
  from torch import nn
label_desc: Use image node_id 2 for calling the Bottleneck obj's underlying member method code with example usage: obj.forward(x) and returns: out