code: def __init__(
      self,
      in_channels,
      out_channels,
      kernel_size=3,
      padding=1,
      dilation=1,
      stride=1,
      groups=1,
      is_bn=True,
      is_relu=True,
  ):
      super(ConvBnRelu2d, self).__init__()
      self.conv = nn.Conv2d(
          in_channels,
          out_channels,
          kernel_size=kernel_size,
          padding=padding,
          stride=stride,
          dilation=dilation,
          groups=groups,
          bias=False,
      )
      self.bn = nn.BatchNorm2d(out_channels, eps=BN_EPS)
      self.relu = nn.ReLU(inplace=True)
      if is_bn is False:
          self.bn = None
      if is_relu is False:
          self.relu = None
  
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new ConvBnRelu2d object from inherited base classes: nn with example: obj = ConvBnRelu2d(in_channels, out_channels, kernel_size, padding, dilation, stride, groups, is_bn, is_relu)