code: def test_svdf_conv_layer_accuracy(self):
      # Test non stacked convolutional layer implementation for accuracy.
      initializer = self._get_initializer()
      optimizer = self._get_optimizer()
      input_tensor = tf.keras.Input(shape=(None, 5))
      layer = svdf_conv.SvdfConvLayer(
          units=4,
          memory_size=3,
          rank=1,
          kernel_initializer=initializer,
          bias_initializer="ones",
          activation="linear",
      )
      output = layer(input_tensor)
      model = tf.keras.models.Model(input_tensor, output)
      weights = _get_test_svdf_layer_weights()
      model.layers[1].set_weights(weights)
      model.compile(optimizer=optimizer, loss="mse")
      model.summary(print_fn=logging.info)
      input_values, _ = get_test_batch_features_and_labels_numpy(
          input_dim=5, output_dim=2
      )
      logging.info("tf.executing_eagerly: %s", tf.executing_eagerly())
      output_tensor = model(input_values)
      observed_output = output_tensor.numpy()
      expected_output = _get_test_svdf_expected_output()
      self.assertAllClose(observed_output, expected_output)
  
file_path: model_search/model_search/ops/svdf_conv_test.py
imports: from absl import logging
  from model_search.ops import svdf_conv
  import numpy
  from six.moves import range
  import tensorflow.compat.v1
label_desc: Use image node_id 5 for calling the SvdfConvLayerTest obj's underlying member method code with example usage: obj.test_svdf_conv_layer_accuracy() without return types