code: def __init__(self, dim, heads=8, dim_head=64, dropout=0.0):
      super().__init__()
      inner_dim = dim_head * heads
      self.heads = heads
      self.norm = nn.LayerNorm(dim)
  
      self.to_qkv = nn.Linear(dim, inner_dim * 3, bias=False)
  
      self.temperature = nn.Parameter(torch.ones(heads, 1, 1))
  
      self.attend = nn.Softmax(dim=-1)
      self.dropout = nn.Dropout(dropout)
  
      self.to_out = nn.Sequential(
          nn.Linear(inner_dim, dim), nn.Dropout(dropout)
      )
  
imports: from random import randrange
  import torch
  from torch import nn, einsum
  from torch.nn import Module, ModuleList
  import torch.nn.functional
  from einops import rearrange, repeat, pack, unpack
  from einops.layers.torch import Rearrange
label_desc: Use image node_id 1 to create a new XCAttention object from inherited base classes: Module with example: obj = XCAttention(dim, heads, dim_head, dropout)