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]
class_docstr_tok: ["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","]"]
docstr: Load data from a file
  
  Args:
      fpath (str): file path to load data from
docstr_tok: ["Load","data","from","a","file","Args",":","fpath","(","str",")",":","file","path","to","load","data","from"]
code: def load(self, fpath: str):
      """
      Load data from a file
  
      Args:
          fpath (str): file path to load data from
      """
      with PathManager.open(fpath, "rb") as hFile:
          data = pickle.load(hFile)
          for name in ["features", "embeddings"]:
              if name in data:
                  getattr(self, name).copy_(
                      torch.tensor(data[name])
                      .float()
                      .to(device=getattr(self, name).device)
                  )
  
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 4 for calling the VertexFeatureEmbedder obj's underlying member method code with example usage: obj.load(fpath) without return types