class_docstr: Abstract base class for all embeddings. Every new type of embedding must implement these methods.
docstr: Returns a list of embedding names.
  
  In most cases, it is just a list with one item, namely the name of
  this embedding. But in some cases, the embedding is made up by different embeddings (StackedEmbedding).
  Then, the list contains the names of all embeddings in the stack.
docstr_tok: ["Returns","a","list","of","embedding","names",".","In","most","cases",",","it","is","just","a","list","with","one","item",",","namely","the","name","of","this","embedding",".","But","in","some","cases",",","the","embedding","is","made","up","by","different","embeddings","(","StackedEmbedding",")",".","Then",",","the","list","contains","the","names","of","all","embeddings","in","the","stack","."]
code: def get_names(self) -> List[str]:
      """Returns a list of embedding names.
  
      In most cases, it is just a list with one item, namely the name of
      this embedding. But in some cases, the embedding is made up by different embeddings (StackedEmbedding).
      Then, the list contains the names of all embeddings in the stack.
      """
      return [self.name]
  
imports: import inspect
  import logging
  from abc import abstractmethod
  from typing import Any, Dict, Generic, List, Sequence, Type, Union
  import torch
  from torch.nn import Parameter, ParameterList
  import flair
  from flair.data import DT, Sentence
label_desc: Use image node_id 7 for calling the Embeddings obj's underlying member method code with example usage: obj.get_names() and returns: list