code: def __init__(
      self,
      latent_dim,
      input_channels,
      filters,
      upsample=True,
      upsample_rgb=True,
      channels=1,
  ):
      super().__init__()
      self.upsample = (
          torch.nn.Upsample(
              scale_factor=2, mode="bilinear", align_corners=False
          )
          if upsample
          else None
      )
  
      self.to_style1 = torch.nn.Linear(latent_dim, input_channels)
      self.to_noise1 = torch.nn.Linear(1, filters)
      self.conv1 = Conv2DModulated(input_channels, filters, 3)
  
      self.to_style2 = torch.nn.Linear(latent_dim, filters)
      self.to_noise2 = torch.nn.Linear(1, filters)
      self.conv2 = Conv2DModulated(filters, filters, 3)
  
      self.activation = torch.nn.LeakyReLU(0.2, inplace=True)
      self.to_rgb = RGBBlock(
          latent_dim, filters, upsample_rgb, channels
      )
  
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 GeneratorBlock object from inherited base classes: torch.nn with example: obj = GeneratorBlock(latent_dim, input_channels, filters, upsample, upsample_rgb, channels)