code: def testSeparableConvQuantize_(self, kwargs):
      kwargs["filters"] = 2
      kwargs["kernel_size"] = 3
      num_samples = 2
      stack_size = 3
      num_row = 7
      num_col = 6
  
      sepconv_model = tf.keras.Sequential(
          [
              tf.keras.Input(
                  shape=(num_row, num_col, stack_size),
                  batch_size=num_samples,
              ),
              tf.keras.layers.SeparableConv2D(**kwargs),
          ]
      )
  
      transformed_model, updated_metadata = ModelTransformer(
          sepconv_model,
          [default_8bit_transforms.SeparableConvQuantize()],
      ).transform()
  
      self.assertContainsSubset(
          updated_metadata.keys(), {"depthwise_conv2d", "conv2d"}
      )
      # Transformed model should have the same output shape
      self.assertEqual(
          sepconv_model.output_shape, transformed_model.output_shape
      )
  
      x = np.random.rand(*sepconv_model.input_shape)
      y = np.random.rand(*sepconv_model.output_shape)
  
      # Ensure model is equivalent, and forward pass results are the same.
      self.assertAllClose(
          sepconv_model.predict(x), transformed_model.predict(x)
      )
  
      # Ensure model is equivalent, and training results are the same.
      sepconv_model.compile(
          loss="categorical_crossentropy", optimizer="sgd"
      )
      transformed_model.compile(
          loss="categorical_crossentropy", optimizer="sgd"
      )
  
      epochs = 100
      for _ in range(epochs):
          sepconv_model.fit(x, y, epochs=1, verbose=2)
          transformed_model.fit(x, y, epochs=1, verbose=2)
          self.assertAllClose(
              sepconv_model.get_weights(),
              transformed_model.get_weights(),
          )
          # To prevent accumulated numerical errors.
          transformed_model.set_weights(sepconv_model.get_weights())
          self.assertAllClose(
              sepconv_model.predict(x), transformed_model.predict(x)
          )
  
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.quantization.keras import quantize_aware_activation
  from tensorflow_model_optimization.python.core.quantization.keras import quantize_layer
  from tensorflow_model_optimization.python.core.quantization.keras import quantizers
  from tensorflow_model_optimization.python.core.quantization.keras.default_8bit import default_8bit_quantize_configs
  from tensorflow_model_optimization.python.core.quantization.keras.default_8bit import default_8bit_quantize_registry
  from tensorflow_model_optimization.python.core.quantization.keras.default_8bit import default_8bit_transforms
  from tensorflow_model_optimization.python.core.quantization.keras.graph_transformations import model_transformer
  from tensorflow_model_optimization.python.core.quantization.keras.layers import conv_batchnorm_test_utils
  from tensorflow_model_optimization.python.core.quantization.keras.layers import dense_batchnorm_test_utils
label_desc: Use image node_id 15 for calling the DefaultTransformsTest obj's underlying member method code with example usage: obj.testSeparableConvQuantize_(kwargs) without return types