code: def __init__(
      self,
      dim,
      heads=8,
      dim_head=64,
      dropout=0.0,
      use_rotary=True,
      use_ds_conv=True,
      conv_query_kernel=5,
  ):
      super().__init__()
      inner_dim = dim_head * heads
      self.use_rotary = use_rotary
      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.use_ds_conv = use_ds_conv
  
      self.to_q = (
          SpatialConv(dim, inner_dim, conv_query_kernel, bias=False)
          if use_ds_conv
          else 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: from math import sqrt, pi, log
  import torch
  from torch import nn, einsum
  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, use_rotary, use_ds_conv, conv_query_kernel)