code: def forward(self, x, context=None, mask=None):
      h = self.heads
  
      q = self.to_q(x)
      context = default(context, x)
      k = self.to_k(context)
      v = self.to_v(context)
  
      q, k, v = map(
          lambda t: rearrange(t, "b n (h d) -> (b h) n d", h=h),
          (q, k, v),
      )
  
      # force cast to fp32 to avoid overflowing
      if _ATTN_PRECISION == "fp32":
          with torch.autocast(enabled=False, device_type="cuda"):
              q, k = q.float(), k.float()
              sim = einsum("b i d, b j d -> b i j", q, k) * self.scale
      else:
          sim = einsum("b i d, b j d -> b i j", q, k) * self.scale
  
      del q, k
  
      if exists(mask):
          mask = rearrange(mask, "b ... -> b (...)")
          max_neg_value = -torch.finfo(sim.dtype).max
          mask = repeat(mask, "b j -> (b h) () j", h=h)
          sim.masked_fill_(~mask, max_neg_value)
  
      # attention, what we cannot get enough of
      sim = sim.softmax(dim=-1)
  
      out = einsum("b i j, b j d -> b i d", sim, v)
      out = rearrange(out, "(b h) n d -> b n (h d)", h=h)
      return self.to_out(out)
  
imports: from inspect import isfunction
  import math
  import torch
  import torch.nn.functional
  from torch import nn, einsum
  from einops import rearrange, repeat
  from typing import Optional, Any
  from ldm.modules.diffusionmodules.util import checkpoint
  import os
label_desc: Use image node_id 2 for calling the CrossAttention obj's underlying member method code with example usage: obj.forward(x, context, mask) and returns: self