code: def compute_index(self, axis, data_object, compute_diff=True):
          
  
          def pandas_index_extraction(df, axis):
              if not axis:
                  return df.index
              else:
                  try:
                      return df.columns
                  except AttributeError:
                      return pandas.Index([])
  
          index_obj = self.index if not axis else self.columns
          old_blocks = self.data if compute_diff else None
          new_indices = data_object.get_indices(
              axis=axis,
              index_func=lambda df: pandas_index_extraction(df, axis),
              old_blocks=old_blocks,
          )
          return index_obj[new_indices] if compute_diff else new_indices
docs: Computes the index after a number of rows have been removed.
  
          Note: In order for this to be used properly, the indexes must not be
              changed before you compute this.
  
          Args:
              axis: The axis to extract the index from.
              data_object: The new data object to extract the index from.
              compute_diff: True to use `self` to compute the index from self
                  rather than data_object. This is used when the dimension of the
                  index may have changed, but the deleted rows/columns are
                  unknown.
  
          Returns:
              A new pandas.Index object.