code: def __init__(
      self,
      in_channel,
      out_channel,
      kernel_size,
      padding,
      kernel_size2=None,
      padding2=None,
      downsample=False,
      fused=False,
  ):
      super().__init__()
  
      pad1 = padding
      pad2 = padding
      if padding2 is not None:
          pad2 = padding2
  
      kernel1 = kernel_size
      kernel2 = kernel_size
      if kernel_size2 is not None:
          kernel2 = kernel_size2
  
      self.conv1 = nn.HybridSequential()
      with self.conv1.name_scope():
          self.conv1.add(
              EqualConv2d(
                  in_channel, out_channel, kernel1, padding=pad1
              )
          )
          self.conv1.add(nn.LeakyReLU(0.2))
  
      if downsample:
          if fused:
              self.conv2 = nn.HybridSequential()
              with self.conv2.name_scope():
                  self.conv2.add(
                      FusedDownsample(
                          out_channel,
                          out_channel,
                          kernel2,
                          padding=pad2,
                      )
                  )
                  self.conv2.add(nn.LeakyReLU(0.2))
  
          else:
              self.conv2 = nn.HybridSequential()
              with self.conv2.name_scope():
                  self.conv2.add(
                      EqualConv2d(
                          out_channel,
                          out_channel,
                          kernel2,
                          padding=pad2,
                      )
                  )
                  self.conv2.add(nn.AvgPool2D(pool_size=(2, 2)))
                  self.conv2.add(nn.LeakyReLU(0.2))
  
      else:
          self.conv2 = nn.HybridSequential()
          with self.conv2.name_scope():
              self.conv2.add(
                  EqualConv2d(
                      out_channel, out_channel, kernel2, padding=pad2
                  )
              )
              self.conv2.add(nn.LeakyReLU(0.2))
  
imports: import numpy
  from math import sqrt
  import mxnet
  import mxnet.ndarray
  import mxnet.gluon.nn
  from numpy import prod
label_desc: Use image node_id 1 to create a new ConvBlock object from inherited base classes: nn with example: obj = ConvBlock(in_channel, out_channel, kernel_size, padding, kernel_size2, padding2, downsample, fused)