code: def __init__(self, latent_dim, input_channel, upsample, channels=3):
      super().__init__()
      self.input_channel = input_channel
      self.to_style = torch.nn.Linear(latent_dim, input_channel)
  
      out_filters = channels
      self.conv = Conv2DModulated(
          input_channel, out_filters, 1, demod=False
      )
  
      self.upsample = (
          torch.nn.Sequential(
              torch.nn.Upsample(
                  scale_factor=2, mode="bilinear", align_corners=False
              ),
              Blur(),
          )
          if upsample
          else None
      )
  
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 RGBBlock object from inherited base classes: torch.nn with example: obj = RGBBlock(latent_dim, input_channel, upsample, channels)