class_docstr: Base class for bottlenecks that implements `forward()` method.
class_docstr_tok: ["Base","class","for","bottlenecks","that","implements","`","forward","(",")","`","method","."]
code: def forward(self, x):
      shortcut = x
  
      out = self.conv1(x)
      out = self.bn1(out)
      out = self.relu(out)
  
      out = self.conv2(out)
      out = self.bn2(out)
      out = self.relu(out)
  
      out = self.conv3(out)
      out = self.bn3(out)
  
      if self.downsample is not None:
          shortcut = self.downsample(x)
  
      out = self.se_module(out) + shortcut
      out = self.relu(out)
  
      return out
  
imports: import math
  from collections import OrderedDict
  import torch
  import torch.nn
  import torch.nn.functional
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import create_classifier
  from ._builder import build_model_with_cfg
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 1 for calling the Bottleneck obj's underlying member method code with example usage: obj.forward(x) and returns: out