code: def __init__(
      self,
      in_size,
      out_size,
      norm,
      kernel_size=3,
      stride=1,
      padding=1,
      act=nn.ReLU,
  ):
      super(ConvBNReLU, self).__init__()
      self.conv1 = nn.Sequential(
          nn.Conv2d(in_size, out_size, kernel_size, stride, padding),
          norm(out_size),
          act(inplace=True),
      )
      # initialise the blocks
      for m in self.children():
          m.apply(weights_init_kaiming)
  
file_path: pywick/pywick/models/segmentation/testnets/exfuse/unet_layer.py
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new ConvBNReLU object from inherited base classes: nn with example: obj = ConvBNReLU(in_size, out_size, norm, kernel_size, stride, padding, act)