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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code: def __init__(
      self,
      embedding_file_path: Union[str, List[str]] = "",
      id_file_path: str = "",
      encoder_model_path: str = "",
      encoder_config_path: str = "",
      use_cuda: bool = False,
  ):
      super().__init__(id_file_path=id_file_path)
  
      self.embeddings = {}
      self.embeddings_by_names = {}
      self.clip_ids = []
      self.encoder = None
      self.encoder_ap = None
      self.use_cuda = use_cuda
  
      if embedding_file_path:
          if isinstance(embedding_file_path, list):
              self.load_embeddings_from_list_of_files(
                  embedding_file_path
              )
          else:
              self.load_embeddings_from_file(embedding_file_path)
  
      if encoder_model_path and encoder_config_path:
          self.init_encoder(
              encoder_model_path, encoder_config_path, use_cuda
          )
  
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 1 to create a new EmbeddingManager object from inherited base classes: BaseIDManager with example: obj = EmbeddingManager(embedding_file_path, id_file_path, encoder_model_path, encoder_config_path, use_cuda)