code: def __init__(
      self,
      dim,
      heads=8,
      dim_head=64,
      dropout=0.0,
      output_num_tokens=None,
  ):
      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_qkv = nn.Linear(dim, inner_dim * 3, bias=False)
  
      self.output_num_tokens = output_num_tokens
      self.ats = (
          AdaptiveTokenSampling(output_num_tokens)
          if exists(output_num_tokens)
          else None
      )
  
      self.to_out = nn.Sequential(
          nn.Linear(inner_dim, dim), nn.Dropout(dropout)
      )
  
imports: import torch
  import torch.nn.functional
  from torch.nn.utils.rnn import pad_sequence
  from torch import nn, einsum
  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, output_num_tokens)