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 = LayerNorm(dim)
  
      self.q_norm = RMSNorm(heads, dim_head)
      self.k_norm = RMSNorm(heads, dim_head)
  
      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, bias=False), nn.Dropout(dropout)
      )
  
imports: from functools import partial
  from typing import List, Union
  import torch
  import torch.nn.functional
  from torch import nn, Tensor
  from torch.nn.utils.rnn import pad_sequence
  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)