code: def __init__(
      self,
      in_channels,
      out_channels,
      kernel_size,
      padding=0,
      stride=1,
      use_batchnorm=True,
  ):
      conv = nn.Conv2D(
          in_channels,
          out_channels,
          kernel_size,
          stride=stride,
          padding=padding,
          bias_attr=not (use_batchnorm),
      )
      relu = nn.ReLU()
  
      bn = nn.BatchNorm2D(out_channels)
  
      super(Conv2DReLU, self).__init__(conv, bn, relu)
  
imports: import copy
  import math
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from medicalseg.cvlibs import manager
  from medicalseg.utils import load_pretrained_model
label_desc: Use image node_id 1 to create a new Conv2DReLU object from inherited base classes: nn with example: obj = Conv2DReLU(in_channels, out_channels, kernel_size, padding, stride, use_batchnorm)