code: def __init__(self, in_channel, style_dim):
      super().__init__()
  
      self.norm = nn.InstanceNorm(in_channels=in_channel)
      self.style = EqualLinear(style_dim, in_channel * 2)
      self.style.initialize()
  
      mx_params = self.style.collect_params()
      for k in mx_params.keys():
          if "bias" in k:
              mx_params[k].data()[:in_channel] = 1
              mx_params[k].data()[in_channel:] = 0
  
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 AdaptiveInstanceNorm object from inherited base classes: nn with example: obj = AdaptiveInstanceNorm(in_channel, style_dim)