code: def __init__(
      self,
      n_bands,
      latent_dim,
      style_depth,
      network_capacity=16,
      channels=1,
      fmap_max=512,
      start_from_zero=True,
  ):
      super().__init__()
      self.image_size = n_bands
      self.latent_dim = latent_dim
      self.num_layers = int(log2(n_bands) - 1)
      self.style_depth = style_depth
  
      self.style_mapping = StyleMapping(
          self.latent_dim, self.style_depth, lr_mul=0.1
      )
  
      filters = [
          network_capacity * (2 ** (i + 1))
          for i in range(self.num_layers)
      ][::-1]
  
      set_fmap_max = partial(min, fmap_max)
      filters = list(map(set_fmap_max, filters))
      init_channels = filters[0]
      filters = [init_channels, *filters]
  
      in_out_pairs = zip(filters[:-1], filters[1:])
  
      self.initial_conv = torch.nn.Conv2d(
          filters[0], filters[0], 3, padding=1
      )
      self.blocks = torch.nn.ModuleList([])
  
      for ind, (in_chan, out_chan) in enumerate(in_out_pairs):
          not_first = ind != 0
          not_last = ind != (self.num_layers - 1)
  
          block = GeneratorBlock(
              latent_dim,
              in_chan,
              out_chan,
              upsample=not_first,
              upsample_rgb=not_last,
              channels=channels,
          )
          self.blocks.append(block)
  
      for m in self.modules():
          if isinstance(m, (torch.nn.Conv2d, torch.nn.Linear)):
              torch.nn.init.kaiming_normal_(
                  m.weight,
                  a=0,
                  mode="fan_in",
                  nonlinearity="leaky_relu",
              )
      for block in self.blocks:
          torch.nn.init.zeros_(block.to_noise1.weight)
          torch.nn.init.zeros_(block.to_noise1.bias)
          torch.nn.init.zeros_(block.to_noise2.weight)
          torch.nn.init.zeros_(block.to_noise2.bias)
  
      initial_block_size = n_bands // self.upsample_factor, 1
      self.initial_block = torch.nn.Parameter(
          torch.randn((1, init_channels, *initial_block_size)),
          requires_grad=False,
      )
      if start_from_zero:
          self.initial_block.data.zero_()
  
imports: import math
  from functools import partial
  from math import log2
  from typing import List
  import torch
  import torch.nn.functional
  from einops import rearrange
  from kornia.filters import filter2d
  from nemo.collections.tts.parts.utils.helpers import mask_sequence_tensor
label_desc: Use image node_id 1 to create a new Generator object from inherited base classes: torch.nn with example: obj = Generator(n_bands, latent_dim, style_depth, network_capacity, channels, fmap_max, start_from_zero)