class_docstr: Abstract class for all encoders
class_docstr_tok: ["Abstract","class","for","all","encoders"]
docstr: Whether the `Encoder` sub class must be fit with `Encoder.encode_train()` before inference
  with `Encoder.encode_inference()`.
docstr_tok: ["Whether","the","`","Encoder","`","sub","class","must","be","fit","with","`","Encoder.encode_train","(",")","`","before","inference","with","`","Encoder.encode_inference","(",")","`","."]
code: def requires_fit(self) -> bool:
      """Whether the `Encoder` sub class must be fit with `Encoder.encode_train()` before inference
      with `Encoder.encode_inference()`."""
      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 8 for calling the Encoder obj's underlying member method code with example usage: obj.requires_fit() without return types