class_docstr: Calibrates a floating point model and then quantizes it.
  
  This is an internal class, not a public interface.
class_docstr_tok: ["Calibrates","a","floating","point","model","and","then","quantizes","it",".","This","is","an","internal","class",",","not","a","public","interface","."]
code: def _create_input_array_from_dict(self, signature_key, inputs):
      input_array = []
      signature_runner = self._interpreter.get_signature_runner(
          signature_key
      )
      input_details = sorted(
          signature_runner.get_input_details().items(),
          key=lambda item: item[1]["index"],
      )
      for input_name, _ in input_details:
          input_array.append(inputs[input_name])
      return input_array
  
file_path: tensorflow/tensorflow/lite/python/optimize/calibrator.py
imports: import numpy
  from tensorflow.lite.python.convert_phase import Component
  from tensorflow.lite.python.convert_phase import convert_phase
  from tensorflow.lite.python.convert_phase import SubComponent
  from tensorflow.lite.python.interpreter import Interpreter
  from tensorflow.python.framework import dtypes
  from tensorflow.python.util.lazy_loader import LazyLoader
label_desc: Use image node_id 2 for calling the Calibrator obj's underlying member method code with example usage: obj._create_input_array_from_dict(signature_key, inputs) and returns: input_array