code: def hybrid_forward(
      self, F, style, noise, step=0, alpha=-1, mixing_range=(-1, -1)
  ):
      out = nd.array(noise[0], ctx=style[0].context)
  
      if style.shape[0] < 2:
          inject_index = [len(self.progression) + 1]
  
      else:
          inject_index = random.sample(
              list(range(step)), style.shape[0] - 1
          )
  
      crossover = 0
  
      for i, (conv, to_rgb) in enumerate(
          zip(self.progression, self.to_rgb)
      ):
          if mixing_range == (-1, -1):
              if (
                  crossover < len(inject_index)
                  and i > inject_index[crossover]
              ):
                  crossover = min(crossover + 1, len(style))
  
              style_step = style[crossover]
  
          else:
              if mixing_range[0] <= i <= mixing_range[1]:
                  style_step = style[1]
  
              else:
                  style_step = style[0]
  
          if i > 0 and step > 0:
              out_prev = out
  
          out = conv(
              out, style_step, nd.array(noise[i], ctx=style[0].context)
          )
  
          if i == step:
              out = to_rgb(out)
  
              if i > 0 and 0 <= alpha < 1:
                  skip_rgb = self.to_rgb[i - 1](out_prev)
                  skip_rgb = F.UpSampling(
                      skip_rgb, scale=2, sample_type="nearest"
                  )
                  out = (1 - alpha) * skip_rgb + alpha * out
  
              break
  
      return out
  
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 2 for calling the Generator obj's underlying member method code with example usage: obj.hybrid_forward(F, style, noise, step, alpha, mixing_range) and returns: out