class_docstr: The standard bottleneck residual block without the last activation layer. It contains 3 conv layers with kernels
  1x1, 3x3, 1x1.
class_docstr_tok: ["The","standard","bottleneck","residual","block","without","the","last","activation","layer",".","It","contains","3","conv","layers","with","kernels","1x1",",","3x3",",","1x1","."]
code: def forward(self, x):
      out = x
      for layer in self.children():
          out = layer(out)
  
      out = x + out
      return out
  
imports: import collections.abc
  import math
  from typing import Dict, List, Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from ...activations import ACT2FN
  from ...modeling_outputs import BackboneOutput, BaseModelOutput
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from ...utils.backbone_utils import BackboneMixin
  from .configuration_vitdet import VitDetConfig
label_desc: Use image node_id 2 for calling the VitDetResBottleneckBlock obj's underlying member method code with example usage: obj.forward(x) and returns: out