class_docstr: Configuration for post-training quantization.
  
  Refer to
  https://www.tensorflow.org/lite/performance/post_training_quantization
  for different post-training quantization options.
docstr: Creates configuration for full integer quantization.
  
  Args:
    representative_data: Representative data used for post-training
      quantization.
    quantization_steps: Number of post-training quantization calibration steps
      to run.
    inference_input_type: Target data type of real-number input arrays.
    inference_output_type: Target data type of real-number output arrays.
    supported_ops: Set of `tf.lite.OpsSet` options, where each option
      represents a set of operators supported by the target device.
  
  Returns:
    QuantizationConfig.
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code: def for_int8(
      cls,
      representative_data: ds.Dataset,
      quantization_steps: int = DEFAULT_QUANTIZATION_STEPS,
      inference_input_type: tf.dtypes.DType = tf.uint8,
      inference_output_type: tf.dtypes.DType = tf.uint8,
      supported_ops: tf.lite.OpsSet = tf.lite.OpsSet.TFLITE_BUILTINS_INT8,
  ) -> "QuantizationConfig":
      """Creates configuration for full integer quantization.
  
      Args:
        representative_data: Representative data used for post-training
          quantization.
        quantization_steps: Number of post-training quantization calibration steps
          to run.
        inference_input_type: Target data type of real-number input arrays.
        inference_output_type: Target data type of real-number output arrays.
        supported_ops: Set of `tf.lite.OpsSet` options, where each option
          represents a set of operators supported by the target device.
  
      Returns:
        QuantizationConfig.
      """
      return QuantizationConfig(
          representative_data=representative_data,
          quantization_steps=quantization_steps,
          inference_input_type=inference_input_type,
          inference_output_type=inference_output_type,
          supported_ops=supported_ops,
      )
  
file_path: mediapipe/mediapipe/model_maker/python/core/utils/quantization.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from typing import Any, Callable, List, Optional, Union
  import tensorflow
  from mediapipe.model_maker.python.core.data import dataset
label_desc: Use image node_id 3 for calling the QuantizationConfig obj's underlying member method code with example usage: obj.for_int8(cls, representative_data, quantization_steps, inference_input_type, inference_output_type, supported_ops) and returns: QuantizationConfig