docstr: Data transformer to fill missing values from a (sequence of) deterministic ``TimeSeries``.
  
  Parameters
  ----------
  fill
      The value used to replace the missing values.
      If set to 'auto', will auto-fill missing values using the :func:`pd.Dataframe.interpolate()` method.
  name
      A specific name for the transformer
  n_jobs
      The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is
      passed as input to a method, parallelising operations regarding different ``TimeSeries``. Defaults to `1`
      (sequential). Setting the parameter to `-1` means using all the available processors.
      Note: for a small amount of data, the parallelisation overhead could end up increasing the total
      required amount of time.
  verbose
      Optionally, whether to print operations progress
  
  Examples
  --------
  >>> import numpy as np
  >>> from darts import TimeSeries
  >>> from darts.dataprocessing.transformers import MissingValuesFiller
  >>> values = np.arange(start=0, stop=1, step=0.1)
  >>> values[5:8] = np.nan
  >>> series = TimeSeries.from_values(values)
  >>> transformer = MissingValuesFiller()
  >>> series_filled = transformer.transform(series)
  >>> print(series_filled)
  <TimeSeries (DataArray) (time: 10, component: 1, sample: 1)>
  array([[[0. ]],
      [[0.1]],
      [[0.2]],
      [[0.3]],
      [[0.4]],
      [[0.5]],
      [[0.6]],
      [[0.7]],
      [[0.8]],
      [[0.9]]])
  Coordinates:
  * time       (time) int64 0 1 2 3 4 5 6 7 8 9
  * component  (component) object '0'
  Dimensions without coordinates: sample
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code: def __init__(
      self,
      fill: Union[str, float] = "auto",
      name: str = "MissingValuesFiller",
      n_jobs: int = 1,
      verbose: bool = False,
  ):
      """Data transformer to fill missing values from a (sequence of) deterministic ``TimeSeries``.
  
      Parameters
      ----------
      fill
          The value used to replace the missing values.
          If set to 'auto', will auto-fill missing values using the :func:`pd.Dataframe.interpolate()` method.
      name
          A specific name for the transformer
      n_jobs
          The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is
          passed as input to a method, parallelising operations regarding different ``TimeSeries``. Defaults to `1`
          (sequential). Setting the parameter to `-1` means using all the available processors.
          Note: for a small amount of data, the parallelisation overhead could end up increasing the total
          required amount of time.
      verbose
          Optionally, whether to print operations progress
  
      Examples
      --------
      >>> import numpy as np
      >>> from darts import TimeSeries
      >>> from darts.dataprocessing.transformers import MissingValuesFiller
      >>> values = np.arange(start=0, stop=1, step=0.1)
      >>> values[5:8] = np.nan
      >>> series = TimeSeries.from_values(values)
      >>> transformer = MissingValuesFiller()
      >>> series_filled = transformer.transform(series)
      >>> print(series_filled)
      <TimeSeries (DataArray) (time: 10, component: 1, sample: 1)>
      array([[[0. ]],
          [[0.1]],
          [[0.2]],
          [[0.3]],
          [[0.4]],
          [[0.5]],
          [[0.6]],
          [[0.7]],
          [[0.8]],
          [[0.9]]])
      Coordinates:
      * time       (time) int64 0 1 2 3 4 5 6 7 8 9
      * component  (component) object '0'
      Dimensions without coordinates: sample
      """
      raise_if_not(
          isinstance(fill, str) or isinstance(fill, float),
          "`fill` should either be a string or a float",
          logger,
      )
      raise_if(
          isinstance(fill, str) and fill != "auto",
          "invalid string for `fill`: can only be set to 'auto'",
          logger,
      )
      # Define fixed params (i.e. attributes defined before calling `super().__init__`):
      self._fill = fill
      super().__init__(name=name, n_jobs=n_jobs, verbose=verbose)
  
file_path: darts/darts/dataprocessing/transformers/missing_values_filler.py
imports: from typing import Any, Mapping, Union
  from darts import TimeSeries
  from darts.logging import get_logger, raise_if, raise_if_not
  from darts.utils.missing_values import fill_missing_values
  from .base_data_transformer import BaseDataTransformer
label_desc: Use image node_id 1 to create a new MissingValuesFiller object from inherited base classes: BaseDataTransformer with example: obj = MissingValuesFiller(fill, name, n_jobs, verbose)