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.
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docstr: This method applies transformation to the non-transformed encoded covariates output of
  `SequentialEncoder._encode_sequence()` after being merged with user-defined covariates. The transformer is
  fitted when `transform()` is called for the first time. This ensures proper transformation of train, validation
  and inference dataset covariates. The masks ensure that no covariates are transformed that user explicitly
  supplied to `TorchForecastingModel.fit()` and `TorchForecastingModel.predict()`
  
  Parameters
  ----------
  covariates
      The non-transformed encoded covariates output of `SequentialEncoder._encode_sequence()` before merging with
      user-defined covariates.
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code: def transform(self, covariates: List[TimeSeries]) -> List[TimeSeries]:
      """This method applies transformation to the non-transformed encoded covariates output of
      `SequentialEncoder._encode_sequence()` after being merged with user-defined covariates. The transformer is
      fitted when `transform()` is called for the first time. This ensures proper transformation of train, validation
      and inference dataset covariates. The masks ensure that no covariates are transformed that user explicitly
      supplied to `TorchForecastingModel.fit()` and `TorchForecastingModel.predict()`
  
      Parameters
      ----------
      covariates
          The non-transformed encoded covariates output of `SequentialEncoder._encode_sequence()` before merging with
          user-defined covariates.
      """
      if not self.fit_called:
          self._update_mask(covariates)
          if any(self.transform_mask):
              # fit the transformer on all encoded values by concatenating multi-series input encodings
              self.transformer.fit(
                  series=TimeSeries.from_values(
                      np.concatenate(
                          [cov.values() for cov in covariates]
                      ),
                      columns=covariates[0].components,
                  ),
                  component_mask=self.transform_mask,
              )
          self._fit_called = True
  
      if any(self.transform_mask):
          transformed = [
              self.transformer.transform(
                  cov, component_mask=self.transform_mask
              )
              for cov in covariates
          ]
      else:
          transformed = covariates
      return transformed
  
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 2 for calling the SequentialEncoderTransformer obj's underlying member method code with example usage: obj.transform(covariates) and returns: transformed