code: def __init__(
      self, in_channels, out_channels, kernel_size, stride=1, padding=""
  ):
      super(ActConvBn, self).__init__()
      self.act = nn.ReLU()
      self.conv = create_conv2d(
          in_channels,
          out_channels,
          kernel_size=kernel_size,
          stride=stride,
          padding=padding,
      )
      self.bn = nn.BatchNorm2d(out_channels, eps=0.001, momentum=0.1)
  
imports: from functools import partial
  import torch
  import torch.nn
  import torch.nn.functional
  from timm.layers import ConvNormAct, create_conv2d, create_pool2d, create_classifier
  from ._builder import build_model_with_cfg
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 1 to create a new ActConvBn object from inherited base classes: nn with example: obj = ActConvBn(in_channels, out_channels, kernel_size, stride, padding)