class_docstr: Computes losses for estimated embeddings given annotated vertices.
  Instances in a minibatch that correspond to the same mesh are grouped
  together. For each group, loss is computed as cross-entropy for
  unnormalized scores given ground truth mesh vertex ids.
  Scores are based on squared distances between estimated vertex embeddings
  and mesh vertex embeddings.
class_docstr_tok: ["Computes","losses","for","estimated","embeddings","given","annotated","vertices",".","Instances","in","a","minibatch","that","correspond","to","the","same","mesh","are","grouped","together",".","For","each","group",",","loss","is","computed","as","cross-entropy","for","unnormalized","scores","given","ground","truth","mesh","vertex","ids",".","Scores","are","based","on","squared","distances","between","estimated","vertex","embeddings","and","mesh","vertex","embeddings","."]
code: def fake_value(
      self,
      densepose_predictor_outputs: Any,
      embedder: nn.Module,
      mesh_name: str,
  ):
      return (
          densepose_predictor_outputs.embedding.sum() * 0
          + embedder(mesh_name).sum() * 0
      )
  
file_path: detectron2/projects/DensePose/densepose/modeling/losses/embed.py
imports: from typing import Any, Dict, List
  import torch
  from torch import nn
  from torch.nn import functional
  from detectron2.config import CfgNode
  from detectron2.structures import Instances
  from densepose.data.meshes.catalog import MeshCatalog
  from densepose.modeling.cse.utils import normalize_embeddings, squared_euclidean_distance_matrix
  from .embed_utils import PackedCseAnnotations
  from .utils import BilinearInterpolationHelper
label_desc: Use image node_id 4 for calling the EmbeddingLoss obj's underlying member method code with example usage: obj.fake_value(densepose_predictor_outputs, embedder, mesh_name) and returns: unknown