code: def __init__(
      self,
      dim,
      proj_kernel,
      kv_proj_stride,
      heads=8,
      dim_head=64,
      dropout=0.0,
  ):
      super().__init__()
      inner_dim = dim_head * heads
      padding = proj_kernel // 2
      self.heads = heads
      self.scale = dim_head**-0.5
  
      self.norm = LayerNorm(dim)
      self.attend = nn.Softmax(dim=-1)
      self.dropout = nn.Dropout(dropout)
  
      self.to_q = DepthWiseConv2d(
          dim,
          inner_dim,
          proj_kernel,
          padding=padding,
          stride=1,
          bias=False,
      )
      self.to_kv = DepthWiseConv2d(
          dim,
          inner_dim * 2,
          proj_kernel,
          padding=padding,
          stride=kv_proj_stride,
          bias=False,
      )
  
      self.to_out = nn.Sequential(
          nn.Conv2d(inner_dim, dim, 1), nn.Dropout(dropout)
      )
  
imports: 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, proj_kernel, kv_proj_stride, heads, dim_head, dropout)