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: Get a random embedding.
  
  Args:
  
  Returns:
      np.ndarray: embedding.
docstr_tok: ["Get","a","random","embedding",".","Args",":","Returns",":","np.ndarray",":","embedding","."]
code: def get_random_embedding(self) -> Any:
      """Get a random embedding.
  
      Args:
  
      Returns:
          np.ndarray: embedding.
      """
      if self.embeddings:
          return self.embeddings[
              random.choices(list(self.embeddings.keys()))[0]
          ]["embedding"]
  
      return None
  
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 14 for calling the EmbeddingManager obj's underlying member method code with example usage: obj.get_random_embedding() and returns: None, self