code: def __init__(self, dim, heads=8, dim_head=64, dropout=0.0):
      super().__init__()
      inner_dim = dim_head * heads
  
      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_q = nn.Linear(dim, inner_dim, bias=False)
      self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)
  
      self.to_out = nn.Sequential(
          nn.Linear(inner_dim, dim), nn.Dropout(dropout)
      )
  
imports: import torch
  from torch import nn
  import torch.nn.functional
  from einops import rearrange, repeat
  from einops.layers.torch import Rearrange
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)