class_docstr: Create an analysis model with the a top level Analysis containing all landmarks at the highest scale
  
  Parameters
  ----------
  hsne : HSne 
      The python HSne wrapper class
  embedder_type : :class:`hsne_analysis.EmbedderType`
      The embedder to be used when creating a new analysis CPU or GPU
      
  Examples
  --------
  Initialize a model using loaded :class:`HSne` data. 
  
  >>> import nptsne
  >>> model = nptsne.hsne_analysis.AnalysisModel(sample_hsne, nptsne.hsne_analysis.EmbedderType.CPU)
  >>> model.top_scale_id
  2
  
  Attributes
  ----------
  top_analysis
  analysis_container
  bottom_scale_id
  top_scale_id
  
  See Also
  --------
  hsne_analysis.Analysis
  hsne_analysis.EmbedderType.CPU
  
  Notes
  -----
  The hsne_analysis.AnalysisModel contains the user driven selections 
  when exploring an HSNE hierarchy. The AnalysisModel is created
  with a top level default hsne_analysis.Analysis containing all top level landmarks.
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docstr: The container for all analyses. 
  
  This is an internal property exposed for debug purposes only
docstr_tok: ["The","container","for","all","analyses",".","This","is","an","internal","property","exposed","for","debug","purposes","only"]
code: def analysis_container(self) -> AnalysisContainer:
      """The container for all analyses.
  
      This is an internal property exposed for debug purposes only"""
      return self._analysis_container
  
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 AnalysisModel obj's underlying member method code with example usage: obj.analysis_container() and returns: self