code: def _quantize_static(
      model: tf.Module, dataset: List[Tuple[tf.Tensor, ...]]
  ):
      def representative_dataset():
          for data_tuple in dataset:
              yield list(data_tuple)
  
      converter = tf.lite.TFLiteConverter.from_keras_model(model)
      converter.optimizations = [tf.lite.Optimize.DEFAULT]
      converter.representative_dataset = representative_dataset
      tflite_quant_model = converter.convert()
      return tflite_quant_model
  
imports: from typing import List, Tuple
  from nebullvm.core.models import QuantizationType
  from nebullvm.optional_modules.tensorflow import tensorflow
label_desc: Use image node_id 2 for calling a global function with example usage: _quantize_static(model, dataset) and returns: tflite_quant_model