class_docstr: MinMaxCalibrationAlgorithm for calculating min and max values of calibration result.
  
  MinMax calibration calculates the global min and global max values.
  
  global min = min of given sample inputs
  global max = max of given sample inputs
class_docstr_tok: ["MinMaxCalibrationAlgorithm","for","calculating","min","and","max","values","of","calibration","result",".","MinMax","calibration","calculates","the","global","min","and","global","max","values",".","global","min","=","min","of","given","sample","inputs","global","max","=","max","of","given","sample","inputs"]
docstr: Calculates the global min and max values.
  
  Returns:
    (min_value, max_value): Min and max calculated using MinMax
docstr_tok: ["Calculates","the","global","min","and","max","values",".","Returns",":","(","min_value",",","max_value",")",":","Min","and","max","calculated","using","MinMax"]
code: def get_min_max_value(self) -> tuple[float, float]:
      """Calculates the global min and max values.
  
      Returns:
        (min_value, max_value): Min and max calculated using MinMax
      """
      return (
          self._statistics.min_max_statistics.global_min,
          self._statistics.min_max_statistics.global_max,
      )
  
imports: import abc
  import itertools
  import logging
  import numpy
  from tensorflow.compiler.mlir.quantization.tensorflow import quantization_options_pb2
  from tensorflow.compiler.mlir.quantization.tensorflow.calibrator import calibration_statistics_pb2
label_desc: Use image node_id 1 for calling the _MinMax obj's underlying member method code with example usage: obj.get_min_max_value() and returns: self,  self