class_docstr: CNN Feature Map Embedding
  Extract feature map from CNN, flatten, project to embedding dim.
class_docstr_tok: ["CNN","Feature","Map","Embedding","Extract","feature","map","from","CNN",",","flatten",",","project","to","embedding","dim","."]
code: def forward(self, x):
      x = self.backbone(x)[-1]
      x = x.flatten(2).transpose(1, 2)
      x = self.proj(x)
      return x
  
imports: import copy
  import logging
  import math
  import torch
  import torch.nn
  from vissl.models.model_helpers import DropPath, to_2tuple, trunc_normal_
  from vissl.models.trunks import register_model_trunk
  from vissl.utils.hydra_config import AttrDict
label_desc: Use image node_id 2 for calling the HybridEmbed obj's underlying member method code with example usage: obj.forward(x) and returns: x