code: def forward(self, x, film_camera_emb=None, N_views_xa=1):
      emb = None
  
      if film_camera_emb is not None:
          if self.emb_dim_in != 1:
              film_camera_emb = (
                  film_camera_emb.reshape(
                      film_camera_emb.shape[0], 2, -1
                  )
                  .flip(1)
                  .reshape(*film_camera_emb.shape)
              )  # swap sin/cos
          film_camera_emb = silu(self.map_layer0(film_camera_emb))
          film_camera_emb = silu(self.map_layer1(film_camera_emb))
          emb = film_camera_emb
  
      # Encoder.
      skips = []
      aux = x
      for name, block in self.enc.items():
          if "aux_down" in name:
              aux = block(aux, N_views_xa)
          elif "aux_skip" in name:
              x = skips[-1] = x + block(aux, N_views_xa)
          elif "aux_residual" in name:
              x = skips[-1] = aux = (
                  x + block(aux, N_views_xa)
              ) / np.sqrt(2)
          else:
              x = (
                  block(x, emb=emb, N_views_xa=N_views_xa)
                  if isinstance(block, UNetBlock)
                  else block(x, N_views_xa=N_views_xa)
              )
              skips.append(x)
  
      # Decoder.
      aux = None
      tmp = None
      for name, block in self.dec.items():
          if "aux_up" in name:
              aux = block(aux, N_views_xa)
          elif "aux_norm" in name:
              tmp = block(x, N_views_xa)
          elif "aux_conv" in name:
              tmp = block(silu(tmp), N_views_xa)
              aux = tmp if aux is None else tmp + aux
          else:
              if x.shape[1] != block.in_channels:
                  # skip connection is pixel-aligned which is good for
                  # foreground features
                  # but it's not good for gradient flow and background features
                  x = torch.cat([x, skips.pop()], dim=1)
              x = block(x, emb=emb, N_views_xa=N_views_xa)
      return aux
  
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 2 for calling the SongUNet obj's underlying member method code with example usage: obj.forward(x, film_camera_emb, N_views_xa) and returns: aux