class_docstr: `SingleEncoder`: Abstract class for single index encoders.
  Single encoders can be used to implement new encoding techniques.
  Each single encoder must implement an `_encode()` method that carries the encoding logic.
  
  The `_encode()` method must take an `index` as input and generate a encoded single `TimeSeries` as output.
class_docstr_tok: ["`","SingleEncoder","`",":","Abstract","class","for","single","index","encoders",".","Single","encoders","can","be","used","to","implement","new","encoding","techniques",".","Each","single","encoder","must","implement","an","`","_encode","(",")","`","method","that","carries","the","encoding","logic",".","The","`","_encode","(",")","`","method","must","take","an","`","index","`","as","input","and","generate","a","encoded","single","`","TimeSeries","`","as","output","."]
docstr: Whether the `SingleEncoder` sub class accepts to be transformed.
docstr_tok: ["Whether","the","`","SingleEncoder","`","sub","class","accepts","to","be","transformed","."]
code: def accept_transformer(self) -> List[bool]:
      """Whether the `SingleEncoder` sub class accepts to be transformed."""
      pass
  
imports: from abc import ABC, abstractmethod
  from typing import List, Optional, Sequence, Tuple, Union
  import numpy
  import pandas
  from darts import TimeSeries
  from darts.dataprocessing.transformers import FittableDataTransformer
  from darts.logging import get_logger, raise_if, raise_log
  from darts.utils.timeseries_generation import generate_index
label_desc: Use image node_id 6 for calling the SingleEncoder obj's underlying member method code with example usage: obj.accept_transformer() without return types