code: def __init__(
      self,
      dim,
      num_heads=8,
      qkv_bias=False,
      qk_scale=None,
      attn_drop=0.0,
      proj_drop=0.0,
  ):
      super().__init__()
      self.num_heads = num_heads
      head_dim = dim // num_heads
      # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
      self.scale = qk_scale or head_dim**-0.5
      self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
      self.attn_drop = nn.Dropout(attn_drop)
      self.proj = nn.Linear(dim, dim)
      self.proj_drop = nn.Dropout(proj_drop)
      self.attn_gradients = None
      self.attention_map = None
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from functools import partial
  from timm.models.vision_transformer import _cfg, PatchEmbed
  from timm.models.registry import register_model
  from timm.models.layers import trunc_normal_, DropPath
  from timm.models.helpers import named_apply, adapt_input_conv
  from fairscale.nn.checkpoint.checkpoint_activations import checkpoint_wrapper
label_desc: Use image node_id 1 to create a new Attention object from inherited base classes: nn with example: obj = Attention(dim, num_heads, qkv_bias, qk_scale, attn_drop, proj_drop)