class_docstr: Abstract base class for all embeddings. Every new type of embedding must implement these methods.
docstr: Set some attributes that would otherwise result in errors. Overwrite these in your embedding class.
docstr_tok: ["Set","some","attributes","that","would","otherwise","result","in","errors",".","Overwrite","these","in","your","embedding","class","."]
code: def __init__(self) -> None:
      """Set some attributes that would otherwise result in errors. Overwrite these in your embedding class."""
      if not hasattr(self, "name"):
          self.name: str = "unnamed_embedding"
      if not hasattr(self, "static_embeddings"):
          # if the embeddings for a sentence are the same in each epoch, set this to True for improved efficiency
          self.static_embeddings = False
      super().__init__()
  
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 1 to create a new Embeddings object from inherited base classes: torch.nn, Generic with example: obj = Embeddings()