class_docstr: This lobe enables the integration of HuggingFace and SpeechBrain
  pretrained LaBSE models.
  
  Source paper LaBSE: https://arxiv.org/abs/2007.01852
  Transformer from HuggingFace needs to be installed:
  https://huggingface.co/transformers/installation.html
  
  The model can be used as a fixed text-based sentence-level embeddings generator or can be finetuned.
  It will download automatically the model from HuggingFace or use a local path.
  
  Arguments
  ---------
  source : str
      HuggingFace hub name: e.g "setu4993/LaBSE"
  save_path : str
      Path (dir) of the downloaded model.
  freeze : bool (default: True)
      If True, the model is frozen. If False, the model will be trained
      alongside with the rest of the pipeline.
  output_norm : bool (default: True)
      If True, normalize the output.
  Example
  -------
  >>> inputs = ["La vie est belle"]
  >>> model_hub = "setu4993/smaller-LaBSE"
  >>> save_path = "savedir"
  >>> model = LaBSE(model_hub, save_path)
  >>> outputs = model(inputs)
code: def __init__(
      self,
      source,
      save_path,
      freeze=True,
      output_norm=True,
  ):
      super().__init__(
          source=source, save_path=save_path, freeze=freeze
      )
  
      self.load_tokenizer(source=source)
  
      self.output_norm = output_norm
  
file_path: speechbrain/speechbrain/lobes/models/huggingface_transformers/labse.py
imports: import torch
  import logging
  import torch.nn.functional
  import os
  from speechbrain.lobes.models.huggingface_transformers.huggingface import HFTransformersInterface
label_desc: Use image node_id 1 to create a new LaBSE object from inherited base classes: HFTransformersInterface with example: obj = LaBSE(source, save_path, freeze, output_norm)