class_docstr: Embedder module that serves as a container for embedders to use with different
  meshes. Extends Module to automatically save / load state dict.
class_docstr_tok: ["Embedder","module","that","serves","as","a","container","for","embedders","to","use","with","different","meshes",".","Extends","Module","to","automatically","save","\/","load","state","dict","."]
docstr: Produce vertex embeddings for the specific mesh; vertex embeddings are
  a tensor of shape [N, D] where:
      N = number of vertices
      D = number of dimensions in the embedding space
  Args:
      mesh_name (str): name of a mesh for which to obtain vertex embeddings
  Return:
      Vertex embeddings, a tensor of shape [N, D]
docstr_tok: ["Produce","vertex","embeddings","for","the","specific","mesh",";","vertex","embeddings","are","a","tensor","of","shape","[","N",",","D","]","where",":","N","=","number","of","vertices","D","=","number","of","dimensions","in","the","embedding","space","Args",":","mesh_name","(","str",")",":","name","of","a","mesh","for","which","to","obtain","vertex","embeddings","Return",":","Vertex","embeddings",",","a","tensor","of","shape","[","N",",","D","]"]
code: def forward(self, mesh_name: str) -> torch.Tensor:
      """
      Produce vertex embeddings for the specific mesh; vertex embeddings are
      a tensor of shape [N, D] where:
          N = number of vertices
          D = number of dimensions in the embedding space
      Args:
          mesh_name (str): name of a mesh for which to obtain vertex embeddings
      Return:
          Vertex embeddings, a tensor of shape [N, D]
      """
      return getattr(self, f"embedder_{mesh_name}")()
  
file_path: detectron2/projects/DensePose/densepose/modeling/cse/embedder.py
imports: import logging
  import numpy
  import pickle
  from enum import Enum
  from typing import Optional
  import torch
  from torch import nn
  from detectron2.config import CfgNode
  from detectron2.utils.file_io import PathManager
  from .vertex_direct_embedder import VertexDirectEmbedder
  from .vertex_feature_embedder import VertexFeatureEmbedder
label_desc: Use image node_id 3 for calling the Embedder obj's underlying member method code with example usage: obj.forward(mesh_name) and returns: getattr