code: def _get_test_svdf_expected_output():
      """Returns output of an svdf layer with the following params.
  
      Note: the values are obtained from _get_svdf_output_using_numpy computation.
      """
      return np.array(
          [
              [
                  [-0.00300881, 0.00605831, 0.01394408, 0.00183136],
                  [-0.0082326, 0.0207185, 0.06872095, 0.00307236],
                  [-0.00363621, 0.05575278, 0.15371153, 0.00205239],
                  [0.01348117, 0.11057685, 0.25696135, 0.01239775],
                  [0.03059855, 0.16540094, 0.36021116, 0.02274311],
                  [0.04771594, 0.22022502, 0.46346098, 0.03308846],
                  [0.06483334, 0.27504909, 0.56671083, 0.04343383],
              ],
              [
                  [-0.00501995, 0.07283279, 0.31317118, 0.01642792],
                  [0.05445613, 0.20556745, 0.57838139, 0.02692774],
                  [0.11618549, 0.43952131, 0.87646019, 0.07446991],
                  [0.13330287, 0.49434543, 0.97971004, 0.08481527],
                  [0.15042025, 0.54916948, 1.08295989, 0.09516063],
                  [0.16753764, 0.60399354, 1.18620968, 0.10550598],
                  [0.18465498, 0.65881759, 1.28945935, 0.11585134],
              ],
          ],
          dtype=np.float32,
      )
  
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 3 for calling a global function with example usage: _get_test_svdf_expected_output() and returns: np