code: def convert_np_to_MvS_as_Series(
      obj: np.ndarray, store=None
  ) -> pd.DataFrame:
      if not isinstance(obj, np.ndarray) and len(obj.shape) > 2:
          raise TypeError("input must be a np.ndarray of dim 1 or 2")
  
      if len(obj.shape) == 1:
          obj = np.reshape(obj, (-1, 1))
  
      res = pd.DataFrame(obj)
  
      # add column names or index from store if stored and length fits
      if (
          isinstance(store, dict)
          and "columns" in store.keys()
          and len(store["columns"]) == obj.shape[1]
      ):
          res.columns = store["columns"]
      if (
          isinstance(store, dict)
          and "index" in store.keys()
          and len(store["index"]) == obj.shape[0]
      ):
          res.index = store["index"]
  
      return res
  
file_path: sktime/sktime/datatypes/_series/_convert.py
imports: import numpy
  import pandas
  from sktime.datatypes._convert_utils._coerce import _coerce_df_dtypes
  from sktime.datatypes._convert_utils._convert import _extend_conversions
  from sktime.datatypes._registry import MTYPE_LIST_SERIES
  from sktime.utils.validation._dependencies import _check_soft_dependencies
label_desc: Use image node_id 6 for calling a global function with example usage: convert_np_to_MvS_as_Series(obj, store) and returns: res