code: def _test_quantizer(self, quantizer):
      inputs = tf.Variable(
          np.array([[-1.0, 0.5], [0.0, 1.0]]),
          name="inputs",
          dtype=tf.dtypes.float32,
      )
      min_var = tf.Variable(0.0)
      max_var = tf.Variable(0.0)
  
      weights = {"min_var": min_var, "max_var": max_var}
      quant_tensor = quantizer(inputs, training=True, weights=weights)
  
      compat.initialize_variables(self)
      results = self.evaluate(quant_tensor)
      min_max_values = self.evaluate([min_var, max_var])
  
      # TODO(pulkitb): Assert on expected values for testing.
      # Since the underlying code is already tested in quant_ops_test.py, this
      # just ensures the Quantizers code is wired properly.
      print("Result: ", results)
      print("min_var: ", min_max_values[0])
      print("max_var: ", min_max_values[1])
  
file_path: model-optimization/tensorflow_model_optimization/python/core/quantization/keras/quantizers_test.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tensorflow_model_optimization.python.core.keras import compat
  from tensorflow_model_optimization.python.core.quantization.keras import quantizers
  from tensorflow_model_optimization.python.core.quantization.keras import utils
label_desc: Use image node_id 2 for calling the QuantizersTest obj's underlying member method code with example usage: obj._test_quantizer(quantizer) without return types