code: def _attention(self, q, k, v, mask=None):
      h = self.heads
  
      if (
          not flash_attn_installed
          or not self.use_flash_attention
          or q.dtype == torch.float32
          or (self.dim_head > 160 or (self.dim_head % 8) != 0)
          or mask is not None
      ):
          # original implementation
          # b n (h d) -> (b h) n d
          q = rearrange_heads_outer(q, h)
          k = rearrange_heads_outer(k, h)
          v = rearrange_heads_outer(v, h)
  
          sim = einsum("b i d, b j d -> b i j", q, k) * self.scale
  
          if exists(mask):
              # standard stable diffusion does not run into here
              mask = mask.view(mask.shape[0], -1)
              b, j = mask.shape
              mask = (
                  mask.unsqueeze(1).expand(b, h, j).reshape(b * h, 1, j)
              )  # b j -> (b h) () j
              sim.masked_fill_(~mask, self.max_neg[sim.dtype])
  
          # attention, what we cannot get enough of
          attn = sim.softmax(dim=-1)
  
          out = einsum("b i j, b j d -> b i d", attn, v)
  
          # (b h) n d -> b n (h d)
          out = rearrange_heads_inner(out, h)
      elif self.context_dim == self.query_dim:
          # self-attention
          qkv = torch.stack([q, k, v], dim=2)
          b, s, t, hd = qkv.shape
          d = hd // h
          qkv = qkv.view(b, s, t, h, d)
  
          out = self.flash_attn(qkv)
          out = out.view(b, s, hd)
      else:
          # cross-attention
          kv = torch.stack([k, v], dim=2)
  
          s_q = q.shape[1]
          b, s_kv, t, hd = kv.shape
          d = hd // h
  
          q = q.view(b, s_q, h, d)
          kv = kv.view(b, s_kv, t, h, d)
  
          out = self.flash_attn(q, kv)
          out = out.view(b, s_q, hd)
  
      return out
  
imports: import math
  from inspect import isfunction
  import torch
  import torch.nn.functional
  from apex.contrib.group_norm import GroupNorm
  from einops import rearrange
  from torch import einsum, nn
  from torch._dynamo import disable
  from nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.util import checkpoint
label_desc: Use image node_id 3 for calling the CrossAttention obj's underlying member method code with example usage: obj._attention(q, k, v, mask) and returns: out