class_docstr: Class responsible for embedding vertex features. Mapping from
  feature space to the embedding space is a tensor of size [K, D], where
      K = number of dimensions in the feature space
      D = number of dimensions in the embedding space
  Vertex features is a tensor of size [N, K], where
      N = number of vertices
      K = number of dimensions in the feature space
  Vertex embeddings are computed as F * E = tensor of size [N, D]
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docstr: Produce vertex embeddings, a tensor of shape [N, D] where:
      N = number of vertices
      D = number of dimensions in the embedding space
  
  Return:
     Full vertex embeddings, a tensor of shape [N, D]
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code: def forward(self) -> torch.Tensor:
      """
      Produce vertex embeddings, a tensor of shape [N, D] where:
          N = number of vertices
          D = number of dimensions in the embedding space
  
      Return:
         Full vertex embeddings, a tensor of shape [N, D]
      """
      return normalize_embeddings(
          torch.mm(self.features, self.embeddings)
      )
  
file_path: detectron2/projects/DensePose/densepose/modeling/cse/vertex_feature_embedder.py
imports: import pickle
  import torch
  from torch import nn
  from detectron2.utils.file_io import PathManager
  from .utils import normalize_embeddings
label_desc: Use image node_id 3 for calling the VertexFeatureEmbedder obj's underlying member method code with example usage: obj.forward() and returns: normalize_embeddings