class_docstr: Style-based GAN
  Reference:
  
      Tero Karras, Samuli Laine, Timo Aila. "A Style-Based Generator 
      Architecture for Generative Adversarial Networks." *CVPR*, 2019
class_docstr_tok: ["Style-based","GAN","Reference",":","Tero","Karras",",","Samuli","Laine",",","Timo","Aila",".","``","A","Style-Based","Generator","Architecture","for","Generative","Adversarial","Networks",".","''","*","CVPR","*",",","2019"]
code: def __init__(self, code_dim=512, n_mlp=8, blur=False):
      super().__init__()
  
      self.generator = Generator(code_dim, blur)
  
      self.style = nn.HybridSequential()
  
      with self.style.name_scope():
          self.style.add(PixelNorm())
  
          for i in range(n_mlp):
              self.style.add(EqualLinear(code_dim, code_dim))
              self.style.add(nn.LeakyReLU(0.2))
  
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 StyledGenerator object from inherited base classes: nn with example: obj = StyledGenerator(code_dim, n_mlp, blur)