class_docstr: Class for representing hierarchical time series.
  
  The hierarchy is represented by the standard aggregation matrix `S`.
  The time series at the bottom (leaf) level of the hierarchy
  (`ts_at_bottom_level`) are assumed to be given by the columns of
  a single pandas dataframe.
  
  The ordering of columns of `ts_at_bottom_level` should be consistent
  with the ordering of the columns of `S`.
  
  Parameters
  ----------
  ts_at_bottom_level
      A single pandas dataframe whose columns are the time series
      corresponding to the leaves of the hierarchy.
  S
      Summation or aggregation matrix whose ordering should be consistent
      with the ordering of the columns of `ts_at_all_levels`.
      In particular, the bottom `k x k` sub-matrix should be identity matrix,
      where `k` is the number of leaves of the hierarchy.
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code: def num_bottom_ts(self):
      return self._S.shape[1]
  
file_path: gluonts/src/gluonts/dataset/hierarchical.py
imports: from typing import Optional
  import numpy
  import pandas
  from gluonts.dataset.pandas import PandasDataset
label_desc: Use image node_id 6 for calling the HierarchicalTimeSeries obj's underlying member method code with example usage: obj.num_bottom_ts() and returns: self