code: def __init__(self, scale=1.0, no_relu=False, conv_block=None):
      super(Block8, self).__init__()
      self.scale = scale
      conv_block = conv_block or ConvNormAct
  
      self.branch0 = conv_block(2080, 192, kernel_size=1, stride=1)
  
      self.branch1 = nn.Sequential(
          conv_block(2080, 192, kernel_size=1, stride=1),
          conv_block(
              192, 224, kernel_size=(1, 3), stride=1, padding=(0, 1)
          ),
          conv_block(
              224, 256, kernel_size=(3, 1), stride=1, padding=(1, 0)
          ),
      )
  
      self.conv2d = nn.Conv2d(448, 2080, kernel_size=1, stride=1)
      self.relu = None if no_relu else nn.ReLU()
  
imports: from functools import partial
  import torch
  import torch.nn
  import torch.nn.functional
  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 ._manipulate import flatten_modules
  from ._registry import register_model, generate_default_cfgs, register_model_deprecations
label_desc: Use image node_id 1 to create a new Block8 object from inherited base classes: nn with example: obj = Block8(scale, no_relu, conv_block)