code: def __init__(
      self,
      in_channels,
      out_channels,
      kernel_size=(3, 3),
      stride=(1, 1),
      padding=(1, 1),
  ):
      super(ConvBn2d, self).__init__()
      self.conv = nn.Conv2d(
          in_channels,
          out_channels,
          kernel_size=kernel_size,
          stride=stride,
          padding=padding,
          bias=False,
      )
      self.bn = nn.BatchNorm2d(out_channels)
  
imports: from torch import nn
  from torch.nn import functional
  import torch
  from torchvision import models
  from torchvision.models import resnet34, resnet101, resnet50, resnet152
  import torchvision
label_desc: Use image node_id 1 to create a new ConvBn2d object from inherited base classes: nn with example: obj = ConvBn2d(in_channels, out_channels, kernel_size, stride, padding)