class_docstr: Abstract base class for all embeddings. Every new type of embedding must implement these methods.
docstr: Add embeddings to all words in a list of sentences.
  
  If embeddings are already added, updates only if embeddings are non-static.
docstr_tok: ["Add","embeddings","to","all","words","in","a","list","of","sentences",".","If","embeddings","are","already","added",",","updates","only","if","embeddings","are","non-static","."]
code: def embed(self, data_points: Union[DT, List[DT]]) -> List[DT]:
      """Add embeddings to all words in a list of sentences.
  
      If embeddings are already added, updates only if embeddings are non-static.
      """
      # if only one sentence is passed, convert to list of sentence
      if not isinstance(data_points, list):
          data_points = [data_points]
  
      if not self._everything_embedded(data_points):
          self._add_embeddings_internal(data_points)
  
      return data_points
  
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 4 for calling the Embeddings obj's underlying member method code with example usage: obj.embed(data_points) and returns: data_points