class_docstr: A dict of dicts to store analyses
  
  Parameters
  ----------
  top_analysis: :class:`Analysis`
      The Analysis at the highest scale level containing all landmarks
      
  Notes
  -----
  The outer dict represents the scales and
  the inner dicts at scale level are indexed by the unique
  self-generated Analysis ids.
class_docstr_tok: ["A","dict","of","dicts","to","store","analyses","Parameters","--","--","--","--","--","top_analysis",":",":","class",":","`","Analysis","`","The","Analysis","at","the","highest","scale","level","containing","all","landmarks","Notes","--","--","-","The","outer","dict","represents","the","scales","and","the","inner","dicts","at","scale","level","are","indexed","by","the","unique","self-generated","Analysis","ids","."]
code: def __str__(self):
      keys = list(self._container.keys())
      # display is from top down
      keys.sort(reverse=True)
      result = ""
      for key in keys:
          result = result + "\nScale: " + str(key) + "\n"
          scale = self._container[key]
          skeys = list(scale.keys())
          skeys.sort()
  
          for skey in skeys:
              analysis = scale[skey]
              result = result + "\t" + str(analysis) + "\n"
      return result
  
file_path: nptsne/src/nptsne/hsne_analysis/analysis_model.py
imports: from ..libs._nptsne._hsne_analysis import EmbedderType, Analysis
  import numpy
label_desc: Use image node_id 6 for calling the AnalysisContainer obj's underlying member method code with example usage: obj.__str__() and returns: result