class_docstr: This layer normalizes embeddings to unit length
code: def forward(self, features: Dict[str, Tensor]):
      features.update(
          {
              "sentence_embedding": F.normalize(
                  features["sentence_embedding"], p=2, dim=1
              )
          }
      )
      return features
  
file_path: sentence-transformers/sentence_transformers/models/Normalize.py
imports: from torch import Tensor
  from torch import nn
  from typing import Dict
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the Normalize obj's underlying member method code with example usage: obj.forward(features) and returns: features