code: def __init__(
      self, dim, seq_len, causal=True, heads=8, dim_head=64, dropout=0.0
  ):
      super().__init__()
      inner_dim = dim_head * heads
      self.heads = heads
      self.seq_len = seq_len
      self.scale = dim_head**-0.5
  
      self.causal = causal
  
      self.to_qkv = nn.Linear(dim, inner_dim * 3, bias=False)
      self.to_out = nn.Sequential(
          nn.Linear(inner_dim, dim), nn.Dropout(dropout)
      )
  
imports: import torch
  import torch.nn.functional
  from einops import rearrange
  from torch import nn
label_desc: Use image node_id 1 to create a new Attention object from inherited base classes: nn with example: obj = Attention(dim, seq_len, causal, heads, dim_head, dropout)