class_docstr: Pre-activation (v2) bottleneck block.
  
  Follows the implementation of "Identity Mappings in Deep Residual Networks":
  https://github.com/KaimingHe/resnet-1k-layers/blob/master/resnet-pre-act.lua
  
  Except it puts the stride on 3x3 conv when available.
class_docstr_tok: ["Pre-activation","(","v2",")","bottleneck","block",".","Follows","the","implementation","of","``","Identity","Mappings","in","Deep","Residual","Networks","''",":","https",":","\/\/github.com\/KaimingHe\/resnet-1k-layers\/blob\/master\/resnet-pre-act.lua","Except","it","puts","the","stride","on","3x3","conv","when","available","."]
code: def zero_init_last(self):
      nn.init.zeros_(self.conv3.weight)
  
imports: from collections import OrderedDict
  from functools import partial
  import torch
  import torch.nn
  from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
  from timm.layers import GroupNormAct, BatchNormAct2d, EvoNorm2dS0, FilterResponseNormTlu2d, ClassifierHead, DropPath, AvgPool2dSame, create_pool2d, StdConv2d, create_conv2d, get_act_layer, get_norm_act_layer, make_divisible
  from ._builder import build_model_with_cfg
  from ._manipulate import checkpoint_seq, named_apply, adapt_input_conv
  from ._registry import generate_default_cfgs, register_model, register_model_deprecations
label_desc: Use image node_id 2 for calling the PreActBottleneck obj's underlying member method code with example usage: obj.zero_init_last() without return types