code: def forward(self, q, kv):
      q_proj = self.q_proj(self.norm(q)).reshape(
          q.shape[0] * self.num_heads, q.shape[1] // self.num_heads, -1
      )
      k_proj, v_proj = (
          self.kv_proj(self.norm(kv))
          .reshape(
              kv.shape[0] * self.num_heads,
              kv.shape[1] // self.num_heads,
              2,
              -1,
          )
          .unbind(2)
      )
      w = AttentionOp.apply(q_proj, k_proj)
      a = torch.einsum("nqk,nck->ncq", w, v_proj)
      x = self.out_proj(a.reshape(*q.shape)).add_(q)
  
      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 CrossAttentionBlock obj's underlying member method code with example usage: obj.forward(q, kv) and returns: x