class_docstr: Converts TensorFlow flatbuffer models from old to new version of schema.
  
  This can convert between any version to the latest version. It uses
  an incremental upgrade strategy to go from version to version.
  
  Usage:
    converter = Converter()
    converter.Convert("a.tflite", "a.json")
    converter.Convert("b.json", "b.tflite")
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docstr: Upgrade data from Version 0 to Version 1.
  
  Changes: Added subgraphs (which contains a subset of formally global
  entries).
  
  Args:
    data: Dictionary representing the TensorFlow lite data to be upgraded.
      This will be modified in-place to be an upgraded version.
docstr_tok: ["Upgrade","data","from","Version","0","to","Version","1",".","Changes",":","Added","subgraphs","(","which","contains","a","subset","of","formally","global","entries",")",".","Args",":","data",":","Dictionary","representing","the","TensorFlow","lite","data","to","be","upgraded",".","This","will","be","modified","in-place","to","be","an","upgraded","version","."]
code: def _Upgrade0To1(self, data):
      """Upgrade data from Version 0 to Version 1.
  
      Changes: Added subgraphs (which contains a subset of formally global
      entries).
  
      Args:
        data: Dictionary representing the TensorFlow lite data to be upgraded.
          This will be modified in-place to be an upgraded version.
      """
      subgraph = {}
      for key_to_promote in [
          "tensors",
          "operators",
          "inputs",
          "outputs",
      ]:
          subgraph[key_to_promote] = data[key_to_promote]
          del data[key_to_promote]
      data["subgraphs"] = [subgraph]
  
imports: import argparse
  import contextlib
  import json
  import os
  import shutil
  import subprocess
  import sys
  import tempfile
  import tensorflow
  from tensorflow.python.platform import resource_loader
label_desc: Use image node_id 4 for calling the Converter obj's underlying member method code with example usage: obj._Upgrade0To1(data) without return types