code: def __init__(
      self,
      in_channels,
      n_filters,
      k_size,
      stride,
      padding,
      bias=True,
      dilation=1,
      with_bn=True,
  ):
      super(conv2DBatchNormRelu, self).__init__()
  
      if dilation > 1:
          conv_mod = nn.Conv2d(
              int(in_channels),
              int(n_filters),
              kernel_size=k_size,
              padding=padding,
              stride=stride,
              bias=bias,
              dilation=dilation,
          )
  
      else:
          conv_mod = nn.Conv2d(
              int(in_channels),
              int(n_filters),
              kernel_size=k_size,
              padding=padding,
              stride=stride,
              bias=bias,
              dilation=1,
          )
  
      if with_bn:
          self.cbr_unit = nn.Sequential(
              conv_mod,
              nn.BatchNorm2d(int(n_filters)),
              nn.ReLU(inplace=True),
          )
      else:
          self.cbr_unit = nn.Sequential(conv_mod, nn.ReLU(inplace=True))
  
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new conv2DBatchNormRelu object from inherited base classes: nn with example: obj = conv2DBatchNormRelu(in_channels, n_filters, k_size, stride, padding, bias, dilation, with_bn)