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)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from timm.models.layers import DropPath
  from lavis.models.ulip_models.pointbert.dvae import Group
  from lavis.models.ulip_models.pointbert.dvae import Encoder
  from lavis.models.ulip_models.pointbert.logger import print_log
  from lavis.models.ulip_models.pointbert.checkpoint import get_missing_parameters_message, get_unexpected_parameters_message
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)