class_docstr: Wrapper for easy vectorization/iteration over instances.
  
  VectorizedDF is an iterable that returns pandas.DataFrame
      in sktime Series or Panel format.
  Elements are all Series or Panels in X, these are iterated over.
  
  Parameters
  ----------
  X : object in sktime compatible Panel or Hierarchical format
      the data container to vectorize over
  y : placeholder argument, not used currently
  is_scitype : str ("Panel", "Hierarchical") or None, default = "Panel"
      scitype of X, if known; if None, will be inferred
      provide to constructor if known to avoid superfluous checks
          Caution: will not conduct checks if provided, assumes checks done
  iterate_as : str ("Series", "Panel"), optional, default="Series
      scitype of the iteration
      for instance, if X is Panel and iterate_as is "Series"
          then the class will iterate over individual Series in X
      or, if X is Hierarchical and iterate_as is "Panel"
          then the class will iterate over individual Panels in X
              (Panel = flat/non-hierarchical collection of Series)
  iterate_cols : boolean, optional, default=False
      whether to iterate over columns, if true, the class will iterate over columns
  
  Methods
  -------
  iter(self) or self.__iter__()
      Iterates over each Series/Panel (depending on iterate_as) in X
      as pandas.DataFrame with Index or MultiIndex (in sktime pandas format)
  len(self) or self.__len__()
      returns number of Series/Panels in X
  get_iter_indices()
      Returns pandas.(Multi)Index that are iterated over
  reconstruct(self, df_list, convert_back=False)
      Takes iterable df_list and returns as an object of is_scitype.
      Used to obtain original format after applying operations to self iterated
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docstr: Coerce obj to a pandas multiindex format.
docstr_tok: ["Coerce","obj","to","a","pandas","multiindex","format","."]
code: def _coerce_to_df(
      self, obj, scitype=None, store=None, store_behaviour=None
  ):
      """Coerce obj to a pandas multiindex format."""
      pandas_dict = {
          "Series": "pd.DataFrame",
          "Panel": "pd-multiindex",
          "Hierarchical": "pd_multiindex_hier",
          None: ["pd.DataFrame", "pd-multiindex", "pd_multiindex_hier"],
      }
  
      if scitype not in pandas_dict.keys():
          raise RuntimeError(
              f"unexpected value found for attribute scitype: {scitype}"
              f"must be one of {pandas_dict.keys()}"
          )
  
      return convert_to(
          obj,
          to_type=pandas_dict[scitype],
          as_scitype=scitype,
          store=store,
          store_behaviour=store_behaviour,
      )
  
imports: import itertools
  import numpy
  import pandas
  from sktime.datatypes._check import check_is_scitype, mtype
  from sktime.datatypes._convert import convert_to
  from sktime.utils.multiindex import flatten_multiindex
  from sktime.utils.parallel import parallelize
label_desc: Use image node_id 4 for calling the VectorizedDF obj's underlying member method code with example usage: obj._coerce_to_df(obj, scitype, store, store_behaviour) and returns: convert_to