docstr: A time series dataset containing tuples of
  (past_target, historic_future_covariates, future_covariates, static_covariates, future_target).
  The "past" series (incl `historic_future_covariates`) have length `input_chunk_length`
  and the "future" series have length `output_chunk_length`. The "future" series are immediately consecutive
  to the "past" series. The slicing of past and future covariates matches that of past and future targets,
  respectively. The slicing itself relies on time indexes to align the series if they have unequal lengths.
  
  Each series must be long enough to contain at least one (input, output) pair; i.e., each
  series must have length at least `input_chunk_length + output_chunk_length`.
  If these conditions are not satisfied, an error will be raised when trying to access some of the splits.
  
  The sampling is uniform over the number of time series; i.e., the i-th sample of this dataset has
  a probability 1/N of coming from any of the N time series in the sequence. If the time series have different
  lengths, they will contain different numbers of slices. Therefore, some particular slices may
  be sampled more often than others if they belong to shorter time series.
  
  Parameters
  ----------
  target_series
      One or a sequence of target `TimeSeries`.
  covariates
      Optionally, one or a sequence of `TimeSeries` containing future-known covariates. If this parameter is set,
      the provided sequence must have the same length as that of `target_series`. Moreover, all
      covariates in the sequence must have a time span large enough to contain all the required slices.
      The joint slicing of the target and covariates is relying on the time axes of both series.
  input_chunk_length
      The length of the emitted past series.
  output_chunk_length
      The length of the emitted future series.
  max_samples_per_ts
      This is an upper bound on the number of tuples that can be produced per time series.
      It can be used in order to have an upper bound on the total size of the dataset and
      ensure proper sampling. If `None`, it will read all of the individual time series in advance (at dataset
      creation) to know their sizes, which might be expensive on big datasets.
      If some series turn out to have a length that would allow more than `max_samples_per_ts`, only the
      most recent `max_samples_per_ts` samples will be considered.
  use_static_covariates
      Whether to use/include static covariate data from input series.
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code: def __init__(
      self,
      target_series: Union[TimeSeries, Sequence[TimeSeries]],
      covariates: Optional[
          Union[TimeSeries, Sequence[TimeSeries]]
      ] = None,
      input_chunk_length: int = 12,
      output_chunk_length: int = 1,
      max_samples_per_ts: Optional[int] = None,
      use_static_covariates: bool = True,
  ):
      """
      A time series dataset containing tuples of
      (past_target, historic_future_covariates, future_covariates, static_covariates, future_target).
      The "past" series (incl `historic_future_covariates`) have length `input_chunk_length`
      and the "future" series have length `output_chunk_length`. The "future" series are immediately consecutive
      to the "past" series. The slicing of past and future covariates matches that of past and future targets,
      respectively. The slicing itself relies on time indexes to align the series if they have unequal lengths.
  
      Each series must be long enough to contain at least one (input, output) pair; i.e., each
      series must have length at least `input_chunk_length + output_chunk_length`.
      If these conditions are not satisfied, an error will be raised when trying to access some of the splits.
  
      The sampling is uniform over the number of time series; i.e., the i-th sample of this dataset has
      a probability 1/N of coming from any of the N time series in the sequence. If the time series have different
      lengths, they will contain different numbers of slices. Therefore, some particular slices may
      be sampled more often than others if they belong to shorter time series.
  
      Parameters
      ----------
      target_series
          One or a sequence of target `TimeSeries`.
      covariates
          Optionally, one or a sequence of `TimeSeries` containing future-known covariates. If this parameter is set,
          the provided sequence must have the same length as that of `target_series`. Moreover, all
          covariates in the sequence must have a time span large enough to contain all the required slices.
          The joint slicing of the target and covariates is relying on the time axes of both series.
      input_chunk_length
          The length of the emitted past series.
      output_chunk_length
          The length of the emitted future series.
      max_samples_per_ts
          This is an upper bound on the number of tuples that can be produced per time series.
          It can be used in order to have an upper bound on the total size of the dataset and
          ensure proper sampling. If `None`, it will read all of the individual time series in advance (at dataset
          creation) to know their sizes, which might be expensive on big datasets.
          If some series turn out to have a length that would allow more than `max_samples_per_ts`, only the
          most recent `max_samples_per_ts` samples will be considered.
      use_static_covariates
          Whether to use/include static covariate data from input series.
      """
  
      super().__init__()
  
      # This dataset is in charge of historical future covariates
      self.ds_past = GenericShiftedDataset(
          target_series=target_series,
          covariates=covariates,
          input_chunk_length=input_chunk_length,
          output_chunk_length=output_chunk_length,
          shift=input_chunk_length,
          shift_covariates=False,
          max_samples_per_ts=max_samples_per_ts,
          covariate_type=CovariateType.HISTORIC_FUTURE,
          use_static_covariates=use_static_covariates,
      )
  
      # This dataset is in charge of serving future covariates
      self.ds_future = GenericShiftedDataset(
          target_series=target_series,
          covariates=covariates,
          input_chunk_length=input_chunk_length,
          output_chunk_length=output_chunk_length,
          shift=input_chunk_length,
          shift_covariates=True,
          max_samples_per_ts=max_samples_per_ts,
          covariate_type=CovariateType.FUTURE,
          use_static_covariates=use_static_covariates,
      )
  
imports: from typing import Optional, Sequence, Tuple, Union
  import numpy
  from darts import TimeSeries
  from .shifted_dataset import GenericShiftedDataset
  from .training_dataset import DualCovariatesTrainingDataset, FutureCovariatesTrainingDataset, MixedCovariatesTrainingDataset, PastCovariatesTrainingDataset, SplitCovariatesTrainingDataset
  from .utils import CovariateType
label_desc: Use image node_id 1 to create a new DualCovariatesSequentialDataset object from inherited base classes: DualCovariatesTrainingDataset with example: obj = DualCovariatesSequentialDataset(target_series, covariates, input_chunk_length, output_chunk_length, max_samples_per_ts, use_static_covariates)