class_docstr: Attention pooling w/ latent query
      
code: def init_weights(self):
      if self.pos_embed is not None:
          trunc_normal_tf_(
              self.pos_embed, std=self.pos_embed.shape[1] ** -0.5
          )
      trunc_normal_tf_(self.latent, std=self.latent_dim**-0.5)
  
file_path: pytorch-image-models/timm/layers/attention_pool.py
imports: from typing import Optional
  import torch
  import torch.nn
  import torch.nn.functional
  from .config import use_fused_attn
  from .mlp import Mlp
  from .weight_init import trunc_normal_tf_
label_desc: Use image node_id 2 for calling the AttentionPoolLatent obj's underlying member method code with example usage: obj.init_weights() without return types