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: Parameters
  ----------
  transformer
      A `FittableDataTransformer` object with a `fit_transform()` and `transform()` method.
  transform_mask
      A boolean 1-D mask specifying which of the input covariates to :meth:`transform()
      <SequentialEncoderTransformer.transform()>` must be transformed.
docstr_tok: ["Parameters","--","--","--","--","--","transformer","A","`","FittableDataTransformer","`","object","with","a","`","fit_transform","(",")","`","and","`","transform","(",")","`","method",".","transform_mask","A","boolean","1-D","mask","specifying","which","of","the","input","covariates","to",":","meth",":","`","transform","(",")","<","SequentialEncoderTransformer.transform","(",")",">","`","must","be","transformed","."]
code: def __init__(
      self,
      transformer: FittableDataTransformer,
      transform_mask: List[bool],
  ):
      """
      Parameters
      ----------
      transformer
          A `FittableDataTransformer` object with a `fit_transform()` and `transform()` method.
      transform_mask
          A boolean 1-D mask specifying which of the input covariates to :meth:`transform()
          <SequentialEncoderTransformer.transform()>` must be transformed.
      """
      self.transformer: FittableDataTransformer = transformer
      self.transform_mask: np.ndarray = np.array(transform_mask)
      self._fit_called: bool = False
  
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 1 to create a new SequentialEncoderTransformer object  with example: obj = SequentialEncoderTransformer(transformer, transform_mask)