code: def __init__(
      self,
      in_channel,
      out_channel,
      kernel_size=3,
      padding=1,
      style_dim=512,
      initial=False,
      upsample=False,
      fused=False,
      blur=False,
  ):
      super().__init__()
  
      self.upsample = None
      if initial:
          self.conv1 = ConstantInput(in_channel)
      else:
          if upsample:
              if fused:
                  self.upsample = "fused"
                  self.conv1 = nn.HybridSequential()
                  with self.conv1.name_scope():
                      self.conv1.add(
                          FusedUpsample(
                              in_channel,
                              out_channel,
                              kernel_size,
                              padding=padding,
                          )
                      )
                      if blur:
                          self.conv1.add(Blur(out_channel))
              else:
                  self.upsample = "nearest"
                  self.conv1 = nn.HybridSequential()
                  with self.conv1.name_scope():
                      self.conv1.add(
                          EqualConv2d(
                              in_dim=in_channel,
                              out_dim=out_channel,
                              kernel=kernel_size,
                              padding=padding,
                          )
                      )
                      if blur:
                          self.conv1.add(Blur(out_channel))
          else:
              self.conv1 = EqualConv2d(
                  in_dim=in_channel,
                  out_dim=out_channel,
                  kernel=kernel_size,
                  padding=padding,
              )
  
      self.noise1 = NoiseInjection(out_channel)
      self.adain1 = AdaptiveInstanceNorm(out_channel, style_dim)
      self.lrelu1 = nn.LeakyReLU(0.2)
  
      self.conv2 = EqualConv2d(
          in_dim=out_channel,
          out_dim=out_channel,
          kernel=kernel_size,
          padding=padding,
      )
      self.noise2 = NoiseInjection(out_channel)
      self.adain2 = AdaptiveInstanceNorm(out_channel, style_dim)
      self.lrelu2 = 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 StyledConvBlock object from inherited base classes: nn with example: obj = StyledConvBlock(in_channel, out_channel, kernel_size, padding, style_dim, initial, upsample, fused, blur)