class_docstr: Abstract class for all encoders
class_docstr_tok: ["Abstract","class","for","all","encoders"]
docstr: If (actual) covariates are given, merge the encoded index with the covariates
  
  Parameters
  ----------
  encoded
      The encoded TimeSeries either from `encode_train()` or `encode_inference()`
  covariates
      Optionally, some past or future covariates supplied by the user.
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code: def _merge_covariates(
      encoded: TimeSeries, covariates: Optional[TimeSeries] = None
  ) -> TimeSeries:
      """If (actual) covariates are given, merge the encoded index with the covariates
  
      Parameters
      ----------
      encoded
          The encoded TimeSeries either from `encode_train()` or `encode_inference()`
      covariates
          Optionally, some past or future covariates supplied by the user.
      """
      return (
          covariates.stack(encoded)
          if covariates is not None
          else encoded
      )
  
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 5 for calling the Encoder obj's underlying member method code with example usage: obj._merge_covariates(encoded, covariates) and returns: unknown