docstr: :param tflite (str): tflite model file
  :param dataset (numpy.ndarray): represent dataset
docstr_tok: [":","param","tflite","(","str",")",":","tflite","model","file",":","param","dataset","(","numpy.ndarray",")",":","represent","dataset"]
code: def __init__(self, tflite, dataset):
      """
      :param tflite (str): tflite model file
      :param dataset (numpy.ndarray): represent dataset
      """
      self.dataset = dataset
      self.interpreter = tf.lite.Interpreter(tflite)
      self.input_details = self.interpreter.get_input_details()
      if len(self.input_details) > 1:
          raise ValueError(
              "Currently, model more than 1 input is unsupported."
          )
      self.output_details = self.interpreter.get_output_details()
      self.interpreter.resize_tensor_input(
          self.input_details[0]["index"], self.input_details[0]["shape"]
      )
      self.interpreter.allocate_tensors()
      self.quantization_param = {}
      for output_detail in self.output_details:
          self.quantization_param[output_detail["name"]] = {}
  
imports: import numpy
  import tensorflow
  import tqdm
label_desc: Use image node_id 1 to create a new Calibrator object from inherited base classes: object with example: obj = Calibrator(tflite, dataset)