code: def __init__(
      self, in_channels, n_filters, k_size, stride, padding, bias=True
  ):
      super(deconv2DBatchNorm, self).__init__()
  
      self.dcb_unit = nn.Sequential(
          nn.ConvTranspose2d(
              int(in_channels),
              int(n_filters),
              kernel_size=k_size,
              padding=padding,
              stride=stride,
              bias=bias,
          ),
          nn.BatchNorm2d(int(n_filters)),
      )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new deconv2DBatchNorm object from inherited base classes: nn with example: obj = deconv2DBatchNorm(in_channels, n_filters, k_size, stride, padding, bias)