code: def __init__(
      self,
      query_dim,
      context_dim=None,
      heads=8,
      dim_head=64,
      dropout=0.0,
      use_flash_attention=False,
  ):
      super().__init__()
      inner_dim = dim_head * heads
      context_dim = default(context_dim, query_dim)
      # make attention part be aware of self-attention/cross-attention
      self.context_dim = context_dim
      self.query_dim = query_dim
      self.dim_head = dim_head
  
      self.scale = dim_head**-0.5
      self.heads = heads
  
      self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
      self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
      self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
  
      self.to_out = nn.Sequential(
          nn.Linear(inner_dim, query_dim), nn.Dropout(dropout)
      )
      self.use_flash_attention = use_flash_attention
  
      if (
          dim_head <= 160
          and (dim_head % 8) == 0
          and flash_attn_installed
      ):
          if context_dim == query_dim:
              self.flash_attn = FlashSelfAttention(
                  softmax_scale=self.scale
              )
          else:
              self.flash_attn = FlashCrossAttention(
                  softmax_scale=self.scale
              )
  
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 1 to create a new CrossAttention object from inherited base classes: nn with example: obj = CrossAttention(query_dim, context_dim, heads, dim_head, dropout, use_flash_attention)