code: def __init__(
      self,
      img_resolution,  # Image resolution at input/output.
      in_channels,  # Number of color channels at input.
      out_channels,  # Number of color channels at output.
      emb_dim_in=0,  # Input embedding dim.
      augment_dim=0,  # Augmentation label dimensionality, 0 = no augmentation.
      model_channels=128,  # Base multiplier for the number of channels.
      channel_mult=[
          1,
          2,
          2,
          2,
      ],  # Per-resolution multipliers for the number of channels.
      channel_mult_emb=4,  # Multiplier for the dimensionality of the embedding vector.
      num_blocks=4,  # Number of residual blocks per resolution.
      attn_resolutions=[16],  # List of resolutions with self-attention.
      dropout=0.10,  # Dropout probability of intermediate activations.
      label_dropout=0,  # Dropout probability of class labels for classifier-free guidance.
      embedding_type="positional",  # Timestep embedding type: 'positional' for DDPM++, 'fourier' for NCSN++.
      channel_mult_noise=0,  # Timestep embedding size: 1 for DDPM++, 2 for NCSN++.
      encoder_type="standard",  # Encoder architecture: 'standard' for DDPM++, 'residual' for NCSN++.
      decoder_type="standard",  # Decoder architecture: 'standard' for both DDPM++ and NCSN++.
      resample_filter=[
          1,
          1,
      ],  # Resampling filter: [1,1] for DDPM++, [1,3,3,1] for NCSN++.
  ):
      assert embedding_type in ["fourier", "positional"]
      assert encoder_type in ["standard", "skip", "residual"]
      assert decoder_type in ["standard", "skip"]
  
      super().__init__()
      self.label_dropout = label_dropout
      self.emb_dim_in = emb_dim_in
      if emb_dim_in > 0:
          emb_channels = model_channels * channel_mult_emb
      else:
          emb_channels = None
      noise_channels = model_channels * channel_mult_noise
      init = dict(init_mode="xavier_uniform")
      init_zero = dict(init_mode="xavier_uniform", init_weight=1e-5)
      init_attn = dict(
          init_mode="xavier_uniform", init_weight=np.sqrt(0.2)
      )
      block_kwargs = dict(
          emb_channels=emb_channels,
          num_heads=1,
          dropout=dropout,
          skip_scale=np.sqrt(0.5),
          eps=1e-6,
          resample_filter=resample_filter,
          resample_proj=True,
          adaptive_scale=False,
          init=init,
          init_zero=init_zero,
          init_attn=init_attn,
      )
  
      # Mapping.
      # self.map_label = Linear(in_features=label_dim, out_features=noise_channels, **init) if label_dim else None
      # self.map_augment = Linear(in_features=augment_dim, out_features=noise_channels, bias=False, **init) if augment_dim else None
      # self.map_layer0 = Linear(in_features=noise_channels, out_features=emb_channels, **init)
      # self.map_layer1 = Linear(in_features=emb_channels, out_features=emb_channels, **init)
      if emb_dim_in > 0:
          self.map_layer0 = Linear(
              in_features=emb_dim_in, out_features=emb_channels, **init
          )
          self.map_layer1 = Linear(
              in_features=emb_channels,
              out_features=emb_channels,
              **init,
          )
  
      if noise_channels > 0:
          self.noise_map_layer0 = Linear(
              in_features=noise_channels,
              out_features=emb_channels,
              **init,
          )
          self.noise_map_layer1 = Linear(
              in_features=emb_channels,
              out_features=emb_channels,
              **init,
          )
  
      # Encoder.
      self.enc = torch.nn.ModuleDict()
      cout = in_channels
      caux = in_channels
      for level, mult in enumerate(channel_mult):
          res = img_resolution >> level
          if level == 0:
              cin = cout
              cout = model_channels
              self.enc[f"{res}x{res}_conv"] = Conv2d(
                  in_channels=cin, out_channels=cout, kernel=3, **init
              )
          else:
              self.enc[f"{res}x{res}_down"] = UNetBlock(
                  in_channels=cout,
                  out_channels=cout,
                  down=True,
                  **block_kwargs,
              )
              if encoder_type == "skip":
                  self.enc[f"{res}x{res}_aux_down"] = Conv2d(
                      in_channels=caux,
                      out_channels=caux,
                      kernel=0,
                      down=True,
                      resample_filter=resample_filter,
                  )
                  self.enc[f"{res}x{res}_aux_skip"] = Conv2d(
                      in_channels=caux,
                      out_channels=cout,
                      kernel=1,
                      **init,
                  )
              if encoder_type == "residual":
                  self.enc[f"{res}x{res}_aux_residual"] = Conv2d(
                      in_channels=caux,
                      out_channels=cout,
                      kernel=3,
                      down=True,
                      resample_filter=resample_filter,
                      fused_resample=True,
                      **init,
                  )
                  caux = cout
          for idx in range(num_blocks):
              cin = cout
              cout = model_channels * mult
              attn = res in attn_resolutions
              self.enc[f"{res}x{res}_block{idx}"] = UNetBlock(
                  in_channels=cin,
                  out_channels=cout,
                  attention=attn,
                  **block_kwargs,
              )
      skips = [
          block.out_channels
          for name, block in self.enc.items()
          if "aux" not in name
      ]
  
      # Decoder.
      self.dec = torch.nn.ModuleDict()
      for level, mult in reversed(list(enumerate(channel_mult))):
          res = img_resolution >> level
          if level == len(channel_mult) - 1:
              self.dec[f"{res}x{res}_in0"] = UNetBlock(
                  in_channels=cout,
                  out_channels=cout,
                  attention=True,
                  **block_kwargs,
              )
              self.dec[f"{res}x{res}_in1"] = UNetBlock(
                  in_channels=cout, out_channels=cout, **block_kwargs
              )
          else:
              self.dec[f"{res}x{res}_up"] = UNetBlock(
                  in_channels=cout,
                  out_channels=cout,
                  up=True,
                  **block_kwargs,
              )
          for idx in range(num_blocks + 1):
              cin = cout + skips.pop()
              cout = model_channels * mult
              attn = idx == num_blocks and res in attn_resolutions
              self.dec[f"{res}x{res}_block{idx}"] = UNetBlock(
                  in_channels=cin,
                  out_channels=cout,
                  attention=attn,
                  **block_kwargs,
              )
          if decoder_type == "skip" or level == 0:
              if (
                  decoder_type == "skip"
                  and level < len(channel_mult) - 1
              ):
                  self.dec[f"{res}x{res}_aux_up"] = Conv2d(
                      in_channels=out_channels,
                      out_channels=out_channels,
                      kernel=0,
                      up=True,
                      resample_filter=resample_filter,
                  )
              self.dec[f"{res}x{res}_aux_norm"] = GroupNorm(
                  num_channels=cout, eps=1e-6
              )
              self.dec[f"{res}x{res}_aux_conv"] = Conv2d(
                  in_channels=cout,
                  out_channels=out_channels,
                  kernel=3,
                  init_weight=0.2,
                  **init,
              )
  
imports: import torch
  import torch.nn
  import torchvision
  import numpy
  import torch
  from torch.nn.functional import silu
  from einops import rearrange, repeat
  from utils.general_utils import matrix_to_quaternion, quaternion_raw_multiply
  from utils.graphics_utils import fov2focal
label_desc: Use image node_id 1 to create a new SongUNet object from inherited base classes: nn with example: obj = SongUNet(img_resolution, in_channels, out_channels, emb_dim_in, augment_dim, model_channels, channel_mult, channel_mult_emb, num_blocks, attn_resolutions, dropout, label_dropout, embedding_type, channel_mult_noise, encoder_type, decoder_type, resample_filter)