class_docstr: Class responsible for embedding vertices. Vertex embeddings take
  the form of a tensor of size [N, D], where
      N = number of vertices
      D = number of dimensions in the embedding space
class_docstr_tok: ["Class","responsible","for","embedding","vertices",".","Vertex","embeddings","take","the","form","of","a","tensor","of","size","[","N",",","D","]",",","where","N","=","number","of","vertices","D","=","number","of","dimensions","in","the","embedding","space"]
docstr: Reset embeddings to random values
docstr_tok: ["Reset","embeddings","to","random","values"]
code: def reset_parameters(self):
      """
      Reset embeddings to random values
      """
      self.embeddings.zero_()
  
file_path: detectron2/projects/DensePose/densepose/modeling/cse/vertex_direct_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 2 for calling the VertexDirectEmbedder obj's underlying member method code with example usage: obj.reset_parameters() without return types