class_docstr: input 35*35*384
  output 35*35*384
class_docstr_tok: ["input","35","*","35","*","384","output","35","*","35","*","384"]
code: def __init__(self, scale=1.0):
      super(InceptionResA, self).__init__()
      self.relu = nn.ReLU(inplace=False)
      self.scale = scale
      self.branch_0 = BasicConv2d(384, 32, kernel_size=1, stride=1)
      self.branch_1 = nn.Sequential(
          BasicConv2d(384, 32, kernel_size=1, stride=1),
          BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1),
      )
      self.branch_2 = nn.Sequential(
          BasicConv2d(384, 32, kernel_size=1, stride=1),
          BasicConv2d(32, 48, kernel_size=3, stride=1, padding=1),
          BasicConv2d(48, 64, kernel_size=3, stride=1, padding=1),
      )
      self.branch_all = BasicConv2d(128, 384, kernel_size=1, stride=1)
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 1 to create a new InceptionResA object from inherited base classes: nn with example: obj = InceptionResA(scale)