code: def _runSvdf(
      self, inputs, num_units, memory_size, rank, use_bias, activation
  ):
      # Force graph mode
      g = tf.Graph()
      with g.as_default():
          with self.session(graph=g) as sess:
              initializer = self._buildInitializer()
              act_func = None
              if activation != "linear":
                  act_func = getattr(tf.nn, activation)
              svdf_layer = svdf_cell.SvdfCell(
                  num_units=num_units,
                  memory_size=memory_size,
                  rank=rank,
                  use_bias=use_bias,
                  activation=act_func,
                  feature_weights_initializer=initializer,
                  time_weights_initializer=initializer,
                  image_summary=True,
              )
  
              state = svdf_layer.zero_state(
                  batch_size=inputs.shape[1], dtype=tf.float32
              )
              i = 0
              for input_t in inputs:
                  logging.debug("state[%d]:\n %s", i, state.eval())
                  input_t_constant = tf.constant(input_t)
                  activations, state = svdf_layer(
                      inputs=input_t_constant, state=state
                  )
                  if i == 0:
                      # Initialize only the first iteration.
                      init = tf.compat.v1.global_variables_initializer()
                      sess.run(init)
                  outputs = self._collectOutputsAndGradients(
                      sess, input_t_constant, activations
                  )
                  i += 1
              return outputs
  
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 3 for calling the SvdfCellTest obj's underlying member method code with example usage: obj._runSvdf(inputs, num_units, memory_size, rank, use_bias, activation) and returns: outputs