                layers.Input(shape=shapes[2][1:])]
      model = models.Model(inputs, norm(inputs))
      data = [10 * np.random.random(shape) for shape in shapes]
  
      actual_output = model.predict(data)
      actual_output_shape = actual_output.shape
  
      for expected_dim, actual_dim in zip(expected_output_shape,
                                          actual_output_shape):
          if expected_dim is not None:
 
</s> remove     actual_output = model.predict(input_data)
 </s> add     actual_output = model.predict(input_data, verbose=0) </s> remove         _output = recovered_model.predict(input_data)
 </s> add         _output = recovered_model.predict(input_data, verbose=0) </s> remove     actual_output = model.predict(input_data)
 </s> add     actual_output = model.predict(input_data, verbose=0) </s> remove             # following bug in TF 2.8 when writing records:
 </s> add             # following bug in TF 2.8/2.9 when writing records: </s> remove         self._tensorboard.on_train_batch_end(self._model.iterations, logs=logs)
        if get_tf_version() == 2.8:
            # Bug in TF 2.8 where batch recording got deleted.
 </s> add         if get_tf_version() > 2.7:
            # Bug in TF 2.8/2.9 where batch recording got deleted.
    actual_output = model.predict(data, verbose=0)
                layers.Input(shape=shapes[2][1:])]
      model = models.Model(inputs, norm(inputs))
      data = [10 * np.random.random(shape) for shape in shapes]
  
    actual_output = model.predict(data, verbose=0)
      actual_output_shape = actual_output.shape
  
      for expected_dim, actual_dim in zip(expected_output_shape,
                                          actual_output_shape):
          if expected_dim is not None:
keep keep keep keep replace keep keep keep keep keep
 Update TF to 2.9
    - Update TF to 2.7 to 2.9
    - Bump dependencies
    - Remove decode from pynvml calls
    - force keras predict functions to non-verbose
    - update tests
    - update Tensorboard logging
    - Update docs 
tests/lib/model/normalization_test.py