code: def forward(self, x, emb=None, N_views_xa=1):
      orig = x
      x = self.conv0(silu(self.norm0(x)))
  
      if emb is not None:
          params = (
              self.affine(emb).unsqueeze(2).unsqueeze(3).to(x.dtype)
          )
          if self.adaptive_scale:
              scale, shift = params.chunk(chunks=2, dim=1)
              x = silu(torch.addcmul(shift, self.norm1(x), scale + 1))
          else:
              x = silu(self.norm1(x.add_(params)))
  
      x = silu(self.norm1(x))
  
      x = self.conv1(
          torch.nn.functional.dropout(
              x, p=self.dropout, training=self.training
          )
      )
      x = x.add_(self.skip(orig) if self.skip is not None else orig)
      x = x * self.skip_scale
  
      if self.num_heads:
          if N_views_xa != 1:
              B, C, H, W = x.shape
              # (B, C, H, W) -> (B/N, N, C, H, W) -> (B/N, N, H, W, C)
              x = x.reshape(
                  B // N_views_xa, N_views_xa, *x.shape[1:]
              ).permute(0, 1, 3, 4, 2)
              # (B/N, N, H, W, C) -> (B/N, N*H, W, C) -> (B/N, C, N*H, W)
              x = x.reshape(
                  B // N_views_xa, N_views_xa * x.shape[2], *x.shape[3:]
              ).permute(0, 3, 1, 2)
          q, k, v = (
              self.qkv(self.norm2(x))
              .reshape(
                  x.shape[0] * self.num_heads,
                  x.shape[1] // self.num_heads,
                  3,
                  -1,
              )
              .unbind(2)
          )
          w = AttentionOp.apply(q, k)
          a = torch.einsum("nqk,nck->ncq", w, v)
          x = self.proj(a.reshape(*x.shape)).add_(x)
          x = x * self.skip_scale
          if N_views_xa != 1:
              # (B/N, C, N*H, W) -> (B/N, N*H, W, C)
              x = x.permute(0, 2, 3, 1)
              # (B/N, N*H, W, C) -> (B/N, N, H, W, C) -> (B/N, N, C, H, W)
              x = x.reshape(
                  B // N_views_xa, N_views_xa, H, W, C
              ).permute(0, 1, 4, 2, 3)
              # (B/N, N, C, H, W) -> # (B, C, H, W)
              x = x.reshape(B, C, H, W)
      return x
  
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 UNetBlock obj's underlying member method code with example usage: obj.forward(x, emb, N_views_xa) and returns: x