class_docstr: Embed a base model and the net vlad to a new network.
  
  Args:
      base_model (`int`):
          The base model that extracts image features.
      net_vlad (`int`):
          NetVLAD model to extract global features.
class_docstr_tok: ["Embed","a","base","model","and","the","net","vlad","to","a","new","network",".","Args",":","base_model","(","`","int","`",")",":","The","base","model","that","extracts","image","features",".","net_vlad","(","`","int","`",")",":","NetVLAD","model","to","extract","global","features","."]
code: def forward(self, x):
      x = self.base_model(x)
      embedded_x = self.net_vlad(x)
      return embedded_x
  
file_path: towhee/towhee/models/layers/netvlad.py
imports: import torch
  import torch.nn.functional
  from torch import nn
label_desc: Use image node_id 2 for calling the EmbedNet obj's underlying member method code with example usage: obj.forward(x) and returns: embedded_x