code: def testRank1(self):
      # Create list of time_dim inputs of shape [batch_size, frame_size].
      # time_dim = 10
      # batch_size = 2
      # frame_size = 3
      # NOTE: this is equivalent to the svdf_test.py setup, and shows that the
      # SvdfCell is equivalent to the non-recurrent Svdf when doing both forward
      # and backward passes on the same data, with the same configuration.
      inputs_list = [
          [
              np.array(
                  [
                      [0.12609188, -0.46347019, -0.89598465],
                      [0.35867718, 0.36897406, 0.73463392],
                  ],
                  dtype=np.float32,
              )
          ],
          [
              np.array(
                  [
                      [0.14278367, -1.64410412, -0.75222826],
                      [-0.57290924, 0.12729003, 0.7567004],
                  ],
                  dtype=np.float32,
              )
          ],
          [
              np.array(
                  [
                      [0.49837467, 0.19278903, 0.26584083],
                      [0.17660543, 0.52949083, -0.77931279],
                  ],
                  dtype=np.float32,
              )
          ],
          [
              np.array(
                  [
                      [-0.11186574, 0.13164264, -0.05349274],
                      [-0.72674477, -0.5683046, 0.55900657],
                  ],
                  dtype=np.float32,
              )
          ],
          [
              np.array(
                  [
                      [-0.68892461, 0.37783599, 0.18263303],
                      [-0.63690937, 0.44483393, -0.71817774],
                  ],
                  dtype=np.float32,
              )
          ],
          [
              np.array(
                  [
                      [-0.81299269, -0.86831826, 1.43940818],
                      [-0.95760226, 1.82078898, 0.71135032],
                  ],
                  dtype=np.float32,
              )
          ],
          [
              np.array(
                  [
                      [-1.45006323, -0.82251364, -1.69082689],
                      [-1.65087092, -1.89238167, 1.54172635],
                  ],
                  dtype=np.float32,
              )
          ],
          [
              np.array(
                  [
                      [0.03966608, -0.24936394, -0.77526885],
                      [2.06740379, -1.51439476, 1.43768692],
                  ],
                  dtype=np.float32,
              )
          ],
          [
              np.array(
                  [
                      [0.11771342, -0.23761693, -0.65898693],
                      [0.31088525, -1.55601168, -0.87661445],
                  ],
                  dtype=np.float32,
              )
          ],
          [
              np.array(
                  [
                      [-0.89477462, 1.67204106, -0.53235275],
                      [-0.6230064, 0.29819036, 1.06939757],
                  ],
                  dtype=np.float32,
              )
          ],
      ]
      expected = {
          "activations": [
              [0.36726, -0.522303, -0.456502, -0.175475],
              [0.170129, -0.344477, 0.385056, -0.281581],
          ],
          "input_grad": [
              [0.179993, 0.115846, 0.169998],
              [0.179993, 0.115846, 0.169998],
          ],
          "SvdfCell/SVDF_weights_feature:0": [
              [-0.918723, -1.700028, -2.022024, 1.395944],
              [-0.667541, -0.83933, -0.461926, -0.542757],
              [0.367526, -0.330445, 1.587667, -0.107099],
          ],
          "SvdfCell/SVDF_weights_time:0": [
              [
                  -0.121947,
                  0.685224,
                  -0.480493,
                  0.429518,
                  0.121952,
                  0.238488,
                  1.969541,
                  -0.030494,
                  0.498736,
                  -0.22269,
              ],
              [
                  0.141328,
                  -0.497931,
                  0.35046,
                  -0.349594,
                  -0.382569,
                  -0.187508,
                  -1.78753,
                  0.483268,
                  -0.427602,
                  -0.066856,
              ],
              [
                  0.04620457,
                  -0.53625858,
                  0.18684755,
                  -0.15690115,
                  -0.35235715,
                  0.60837597,
                  -1.67157829,
                  0.36053753,
                  -0.97437823,
                  0.32919434,
              ],
              [
                  -0.15174544,
                  0.25558627,
                  -0.23719919,
                  0.27823007,
                  0.48153371,
                  0.34780267,
                  1.31657171,
                  -0.7149213,
                  0.12750566,
                  0.36063662,
              ],
          ],
          "SvdfCell/SVDF_bias:0": [2.0, 2.0, 2.0, 2.0],
      }
      inputs = np.vstack(inputs_list)
      self._runTest(
          num_units=4,
          rank=1,
          memory_size=inputs.shape[0],
          inputs=inputs,
          use_bias=True,
          activation="linear",
          expected=expected,
      )
  
file_path: model_search/model_search/ops/svdf_cell_test.py
imports: import collections
  from absl import logging
  from model_search.ops import svdf_cell
  import numpy
  import tensorflow.compat.v2
label_desc: Use image node_id 5 for calling the SvdfCellTest obj's underlying member method code with example usage: obj.testRank1() without return types