class_docstr: Base `Embedding` Manager class. Every new `Embedding` manager must inherit this.
  It defines common `Embedding` manager specific functions.
  
  It expects embeddings files in the following format:
  
  ::
  
      {
          'audio_file_key':{
              'name': 'category_name',
              'embedding'[<embedding_values>]
          },
          ...
      }
  
  `audio_file_key` is a unique key to the audio file in the dataset. It can be the path to the file or any other unique key.
  `embedding` is the embedding vector of the audio file.
  `name` can be name of the speaker of the audio file.
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docstr: Load embeddings from a json file.
  
  Args:
      file_path (str): Path to the target json file.
docstr_tok: ["Load","embeddings","from","a","json","file",".","Args",":","file_path","(","str",")",":","Path","to","the","target","json","file","."]
code: def load_embeddings_from_file(self, file_path: str) -> None:
      """Load embeddings from a json file.
  
      Args:
          file_path (str): Path to the target json file.
      """
      (
          self.name_to_id,
          self.clip_ids,
          self.embeddings,
          self.embeddings_by_names,
      ) = self.read_embeddings_from_file(file_path)
  
imports: import json
  import random
  from typing import Any, Dict, List, Tuple, Union
  import fsspec
  import numpy
  import torch
  from TTS.config import load_config
  from TTS.encoder.utils.generic_utils import setup_encoder_model
  from TTS.utils.audio import AudioProcessor
label_desc: Use image node_id 8 for calling the EmbeddingManager obj's underlying member method code with example usage: obj.load_embeddings_from_file(file_path) without return types