code: def __init__(
      self,
      in_c,
      out_c,
      kernel=(1, 1),
      stride=(1, 1),
      padding=(0, 0),
      groups=1,
  ):
      super(LinearBlock, self).__init__()
      self.layers = nn.Sequential(
          Conv2d(
              in_c,
              out_c,
              kernel,
              stride,
              padding,
              groups=groups,
              bias=False,
          ),
          BatchNorm2d(num_features=out_c),
      )
  
imports: import torch.nn
  from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Sequential, Module
  import torch
label_desc: Use image node_id 1 to create a new LinearBlock object from inherited base classes: Module with example: obj = LinearBlock(in_c, out_c, kernel, stride, padding, groups)