class_docstr: Create features based on time index or hierarchy values.
  
  Returns index of `X` in `transform` as transformed features.
  By default, time features only.
  Can also be used to select hierarchy levels in case of hierarchical input,
  via the `levels` argument.
  
  Return columns of `transform` applied to `pandas` based containers
  have same name as level if levels have name in `transform` input,
  otherwise `index` (time) and `level_{N}` where N is the level index integer.
  
  To *add* instead of *replace*, use `FeatureUnion` and/or the `+` dunder.
  
  Under the default setting of `coerce_to_type="auto"`:
  
  * date-like indices incl periods are coerced to float (via int64)
    this typically results in units of periods since start of 1970 (first = 0)
  * object, string, and category indices are coerced to integer (unique category ID)
    mapping onto integers is per category levels, after `pandas` category coercion
  
  For more custom options or a direct `pandas` interface,
  an alternative is `PandasTransformAdaptor` with `method="reset_index"`.
  
  Parameters
  ----------
  coerce_to_type : str, or dict, optional, default="auto"
      how to coerce the index columns to when passed to `X`
      default="auto" coerces:
      date-like indices to float (via int64)
      object, string, and category indices to integer
      values other than "auto" are passed to `DataFrame.astype` in `transform`
  level : None (default), int, str, or iterable of pandas index level name elements
      if passed, selects the hierarchy levels that will be turned into columns in `X`
      if passed, passed on as `level` to `reset_index` internally
      if None, will convert only the time index (last level) into features
      Note that this is different from the default of `reset_index`
  ix_source : str, optional, default="X"
      which object to take the index from
      default = "X" = `X` as passed to `transform`
          if used within `ForecastingPipeline`, this means `X` by default
      "y" = `y` as passed to `transform`, if passed (not `None`), otherwise `X`
  
  Examples
  --------
  >>> from sktime.datasets import load_airline
  >>> from sktime.transformations.compose import IxToX
  >>>
  >>> X = load_airline()
  >>> t = IxToX()
  >>> Xt = t.fit_transform(X)
code: def __init__(self, coerce_to_type="auto", level=None, ix_source="X"):
      self.coerce_to_type = coerce_to_type
      self.level = level
      self.ix_source = ix_source
  
      super().__init__()
  
file_path: sktime/sktime/transformations/compose/_ixtox.py
imports: import pandas
  from pandas.api.types import is_datetime64_any_dtype
  from sktime.transformations.base import BaseTransformer
label_desc: Use image node_id 1 to create a new IxToX object from inherited base classes: BaseTransformer with example: obj = IxToX(coerce_to_type, level, ix_source)