code: def __init__(self, fused=True, from_rgb_activate=False):
      super().__init__()
  
      self.progression = nn.HybridSequential()
      with self.progression.name_scope():
          self.progression.add(
              ConvBlock(16, 32, 3, 1, downsample=True, fused=fused)
          )  # 512
          self.progression.add(
              ConvBlock(32, 64, 3, 1, downsample=True, fused=fused)
          )  # 256
          self.progression.add(
              ConvBlock(64, 128, 3, 1, downsample=True, fused=fused)
          )  # 128
          self.progression.add(
              ConvBlock(128, 256, 3, 1, downsample=True, fused=fused)
          )  # 64
          self.progression.add(
              ConvBlock(256, 512, 3, 1, downsample=True)
          )  # 32
          self.progression.add(
              ConvBlock(512, 512, 3, 1, downsample=True)
          )  # 16
          self.progression.add(
              ConvBlock(512, 512, 3, 1, downsample=True)
          )  # 8
          self.progression.add(
              ConvBlock(512, 512, 3, 1, downsample=True)
          )  # 4
          self.progression.add(ConvBlock(513, 512, 3, 1, 4, 0))
  
      def make_from_rgb(out_channel):
          if from_rgb_activate:
              module = nn.HybridSequential()
              with module.name_scope():
                  module.add(EqualConv2d(3, out_channel, 1))
                  module.add(nn.LeakyReLU(0.2))
              return module
  
          else:
              return EqualConv2d(3, out_channel, 1)
  
      self.from_rgb = nn.HybridSequential()
      with self.from_rgb.name_scope():
          self.from_rgb.add(make_from_rgb(16))
          self.from_rgb.add(make_from_rgb(32))
          self.from_rgb.add(make_from_rgb(64))
          self.from_rgb.add(make_from_rgb(128))
          self.from_rgb.add(make_from_rgb(256))
          self.from_rgb.add(make_from_rgb(512))
          self.from_rgb.add(make_from_rgb(512))
          self.from_rgb.add(make_from_rgb(512))
          self.from_rgb.add(make_from_rgb(512))
  
      self.n_layer = len(self.progression)
  
      self.linear = EqualLinear(512, 1)
  
file_path: gluon-cv/scripts/gan/stylegan/model.py
imports: import random
  import numpy
  from math import sqrt
  import mxnet
  import mxnet.ndarray
  from modules import *
label_desc: Use image node_id 1 to create a new Discriminator object from inherited base classes: nn with example: obj = Discriminator(fused, from_rgb_activate)