class_docstr: resnet block contains inception
class_docstr_tok: ["resnet","block","contains","inception"]
code: def __init__(self, kernel_size, in_channels, out_channels):
      super(Conv_transition, self).__init__()
      if not kernel_size:
          kernel_size = [1, 3, 5]
      paddings = [int(a / 2) for a in kernel_size]
      # self.Conv0=nn.Conv2d(in_channels,out_channels,3,stride=1,padding=1)
      self.Conv1 = nn.Conv2d(
          in_channels,
          out_channels,
          kernel_size[0],
          stride=1,
          padding=paddings[0],
      )
      self.Conv2 = nn.Conv2d(
          in_channels,
          out_channels,
          kernel_size[1],
          stride=1,
          padding=paddings[1],
      )
      self.Conv3 = nn.Conv2d(
          in_channels,
          out_channels,
          kernel_size[2],
          stride=1,
          padding=paddings[2],
      )
      self.Conv_f = nn.Conv2d(
          3 * out_channels, out_channels, 3, stride=1, padding=1
      )
      self.bn = nn.BatchNorm2d(out_channels)
      self.act = nn.PReLU()
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 1 to create a new Conv_transition object from inherited base classes: nn with example: obj = Conv_transition(kernel_size, in_channels, out_channels)