code: def as_compatible_data(data, fastpath=False):
      
      if fastpath and getattr(data, 'ndim', 0) > 0:
          # can't use fastpath (yet) for scalars
          return _maybe_wrap_data(data)
  
      if isinstance(data, Variable):
          return data.data
  
      if isinstance(data, NON_NUMPY_SUPPORTED_ARRAY_TYPES):
          return _maybe_wrap_data(data)
  
      if isinstance(data, tuple):
          data = utils.to_0d_object_array(data)
  
      if isinstance(data, pd.Timestamp):
          # TODO: convert, handle datetime objects, too
          data = np.datetime64(data.value, 'ns')
  
      if isinstance(data, timedelta):
          data = np.timedelta64(getattr(data, 'value', data), 'ns')
  
      # we don't want nested self-described arrays
      data = getattr(data, 'values', data)
  
      if isinstance(data, np.ma.MaskedArray):
          mask = np.ma.getmaskarray(data)
          if mask.any():
              dtype, fill_value = dtypes.maybe_promote(data.dtype)
              data = np.asarray(data, dtype=dtype)
              data[mask] = fill_value
          else:
              data = np.asarray(data)
  
      # validate whether the data is valid data types
      data = np.asarray(data)
  
      if isinstance(data, np.ndarray):
          if data.dtype.kind == 'O':
              data = _possibly_convert_objects(data)
          elif data.dtype.kind == 'M':
              data = np.asarray(data, 'datetime64[ns]')
          elif data.dtype.kind == 'm':
              data = np.asarray(data, 'timedelta64[ns]')
  
      return _maybe_wrap_data(data)
docs: Prepare and wrap data to put in a Variable.
  
      - If data does not have the necessary attributes, convert it to ndarray.
      - If data has dtype=datetime64, ensure that it has ns precision. If it's a
        pandas.Timestamp, convert it to datetime64.
      - If data is already a pandas or xarray object (other than an Index), just
        use the values.
  
      Finally, wrap it up with an adapter if necessary.