class_docstr: `SequentialEncoderTransformer` applies transformation to the non-transformed encoded covariates output of
  `SequentialEncoder.encode_train()` and `SequentialEncoder.encode_inference()`. The transformer is fitted
  when `transform()` is called for the first time. This ensures proper transformation of train, validation and
  inference dataset covariates. User-supplied covariates are not transformed.
class_docstr_tok: ["`","SequentialEncoderTransformer","`","applies","transformation","to","the","non-transformed","encoded","covariates","output","of","`","SequentialEncoder.encode_train","(",")","`","and","`","SequentialEncoder.encode_inference","(",")","`",".","The","transformer","is","fitted","when","`","transform","(",")","`","is","called","for","the","first","time",".","This","ensures","proper","transformation","of","train",",","validation","and","inference","dataset","covariates",".","User-supplied","covariates","are","not","transformed","."]
docstr: Return whether the transformer has been fitted.
docstr_tok: ["Return","whether","the","transformer","has","been","fitted","."]
code: def fit_called(self) -> bool:
      """Return whether the transformer has been fitted."""
      return self._fit_called
  
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 4 for calling the SequentialEncoderTransformer obj's underlying member method code with example usage: obj.fit_called() and returns: self