code: def test_tcn_keras_forecaster_quantization(self):
      # Capturing behaviors during `pytest -v` influence generating the valid sampling log
      # during quantization. Work around by using `pytest -s` to test this ut.
      train_data, _, test_data = create_data()
      forecaster = TCNForecaster(
          past_seq_len=10,
          future_seq_len=2,
          input_feature_num=10,
          output_feature_num=2,
      )
  
      forecaster.fit(train_data, epochs=1)
      forecaster.quantize(
          input_data=train_data[0], target_data=train_data[1]
      )
      assert forecaster.accelerated_model
      assert forecaster.accelerate_method == "tensorflow_int8"
      pred_q = forecaster.predict(test_data[0], quantize=True)
      eval_q = forecaster.evaluate(test_data, quantize=True)
      assert pred_q.shape == test_data[1].shape
  
imports: import pytest
  import tempfile
  import os
  from unittest import TestCase
  import numpy
  from bigdl.chronos.utils import LazyImport
  from test.bigdl.chronos import op_tf2, op_distributed, op_inference
label_desc: Use image node_id 12 for calling the TestTCNForecaster obj's underlying member method code with example usage: obj.test_tcn_keras_forecaster_quantization() without return types