code: def __init__(self, dim, heads=8, dim_head=64, dropout=0.0):
      super().__init__()
      inner_dim = dim_head * heads
      project_out = not (heads == 1 and dim_head == dim)
  
      self.heads = heads
      self.scale = dim_head**-0.5
  
      self.norm = nn.LayerNorm(dim)
      self.attend = nn.Softmax(dim=-1)
      self.dropout = nn.Dropout(dropout)
  
      self.to_qkv = nn.Linear(dim, inner_dim * 3, bias=False)
  
      self.to_out = (
          nn.Sequential(nn.Linear(inner_dim, dim), nn.Dropout(dropout))
          if project_out
          else nn.Identity()
      )
  
imports: import torch
  from torch import nn
  from einops import rearrange, repeat
  from einops.layers.torch import Rearrange, Reduce
label_desc: Use image node_id 1 to create a new Attention object from inherited base classes: nn with example: obj = Attention(dim, heads, dim_head, dropout)