code: def __init__(self, dropout=0.0, causal=False, use_flash=False):
      super().__init__()
      self.dropout = dropout
      self.attn_dropout = nn.Dropout(dropout)
  
      self.causal = causal
      self.register_buffer("mask", None, persistent=False)
  
      self.use_flash = use_flash
      assert not (
          use_flash
          and version.parse(torch.__version__) < version.parse("2.0.0")
      ), "in order to use flash attention, you must be using pytorch 2.0 or above"
  
      # determine efficient attention configs for cuda and cpu
      self.config = namedtuple(
          "EfficientAttentionConfig",
          ["enable_flash", "enable_math", "enable_mem_efficient"],
      )
      self.cpu_config = self.config(True, True, True)
      self.cuda_config = None
  
      if not torch.cuda.is_available() or not use_flash:
          return
  
      device_properties = torch.cuda.get_device_properties(
          torch.device("cuda")
      )
  
      if device_properties.major == 8 and device_properties.minor == 0:
          print_once(
              "A100 GPU detected, using flash attention if input tensor is on cuda"
          )
          self.cuda_config = self.config(True, False, False)
      else:
          print_once(
              "Non-A100 GPU detected, using math or mem efficient attention if input tensor is on cuda"
          )
          self.cuda_config = self.config(False, True, True)
  
imports: from collections import namedtuple
  from functools import wraps
  import torch
  import torch.nn.functional
  from einops import rearrange, repeat
  from einops.layers.torch import Rearrange
  from packaging import version
  from torch import einsum, nn
label_desc: Use image node_id 1 to create a new Attend object from inherited base classes: nn with example: obj = Attend(dropout, causal, use_flash)