code: def __init__(self, conv_block=ConvNormAct):
      super(ReductionB, self).__init__()
  
      self.branch0 = nn.Sequential(
          conv_block(1024, 192, kernel_size=1, stride=1),
          conv_block(192, 192, kernel_size=3, stride=2),
      )
  
      self.branch1 = nn.Sequential(
          conv_block(1024, 256, kernel_size=1, stride=1),
          conv_block(
              256, 256, kernel_size=(1, 7), stride=1, padding=(0, 3)
          ),
          conv_block(
              256, 320, kernel_size=(7, 1), stride=1, padding=(3, 0)
          ),
          conv_block(320, 320, kernel_size=3, stride=2),
      )
  
      self.branch2 = nn.MaxPool2d(3, stride=2)
  
imports: from functools import partial
  import torch
  import torch.nn
  from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
  from timm.layers import create_classifier, ConvNormAct
  from ._builder import build_model_with_cfg
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 1 to create a new ReductionB object from inherited base classes: nn with example: obj = ReductionB(conv_block)