class_docstr: input 299*299*3
  output 35*35*384
class_docstr_tok: ["input","299","*","299","*","3","output","35","*","35","*","384"]
code: def __init__(self):
      super(StemBlock, self).__init__()
      self.model_a = nn.Sequential(
          BasicConv2d(3, 32, kernel_size=3, stride=2),
          BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1),
          BasicConv2d(32, 64, kernel_size=3, stride=1),
      )
      self.branch_a0 = nn.MaxPool2d(3, stride=2)
      self.branch_a1 = BasicConv2d(64, 96, kernel_size=3, stride=2)
      self.branch_b0 = nn.Sequential(
          BasicConv2d(160, 64, kernel_size=1, stride=1),
          BasicConv2d(64, 96, kernel_size=3, stride=1),
      )
      self.branch_b1 = nn.Sequential(
          BasicConv2d(160, 64, kernel_size=1, stride=1),
          BasicConv2d(
              64, 64, kernel_size=(1, 7), stride=1, padding=(0, 3)
          ),
          BasicConv2d(
              64, 64, kernel_size=(7, 1), stride=1, padding=(3, 0)
          ),
          BasicConv2d(64, 96, kernel_size=3, stride=1),
      )
      self.branch_c0 = BasicConv2d(192, 192, kernel_size=3, stride=2)
      self.branch_c1 = nn.MaxPool2d(3, stride=2)
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 1 to create a new StemBlock object from inherited base classes: nn with example: obj = StemBlock()