code: def __init__(
      self,
      dim,
      num_heads=8,
      qkv_bias=False,
      attn_drop=0.0,
      proj_drop=0.0,
  ):
      super().__init__()
      self.num_heads = num_heads
      self.temperature = nn.Parameter(torch.ones(num_heads, 1, 1))
  
      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 math
  from collections import OrderedDict
  from functools import partial
  from typing import Tuple
  import torch
  import torch.nn.functional
  from torch import nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import trunc_normal_tf_, DropPath, LayerNorm2d, Mlp, SelectAdaptivePool2d, create_conv2d, use_fused_attn, NormMlpClassifierHead, ClassifierHead
  from ._builder import build_model_with_cfg
  from ._features_fx import register_notrace_module
  from ._manipulate import named_apply, checkpoint_seq
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 1 to create a new CrossCovarianceAttn object from inherited base classes: nn with example: obj = CrossCovarianceAttn(dim, num_heads, qkv_bias, attn_drop, proj_drop)