code: def testSeparableConv1DQuantize_(self, kwargs):
      kwargs["filters"] = 2
      kwargs["kernel_size"] = 3
      num_samples = 2
      stack_size = 3
      num_row = 7
  
      sepconv_model = tf.keras.Sequential(
          [
              tf.keras.Input(
                  shape=(num_row, stack_size), batch_size=num_samples
              ),
              tf.keras.layers.SeparableConv1D(**kwargs),
          ]
      )
  
      transformed_model, updated_metadata = ModelTransformer(
          sepconv_model,
          [default_8bit_transforms.SeparableConv1DQuantize()],
      ).transform()
  
      self.assertContainsSubset(
          {
              "sepconv1d_expand_1",
              "separable_conv1d_QAT_SepConv2D",
              "sepconv1d_squeeze_1",
          },
          updated_metadata.keys(),
      )
      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"
      )
      sepconv_model.fit(x, y, epochs=100)
      transformed_model.compile(
          loss="categorical_crossentropy", optimizer="sgd"
      )
      transformed_model.fit(x, y, epochs=100)
  
      # Over a long training cycle with constraints and regularizers, the model
      # can build very minute differences. Hence reducing tol to 1e-5.
      self.assertAllClose(
          sepconv_model.predict(x),
          transformed_model.predict(x),
          atol=1e-5,
          rtol=1e-5,
      )
  
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 14 for calling the DefaultTransformsTest obj's underlying member method code with example usage: obj.testSeparableConv1DQuantize_(kwargs) without return types