  
      # check with the functional API
      model = Model(inp, outp)
  
      actual_output = model.predict(input_data)
  
      # test serialization, weight setting at model level
      model_config = model.get_config()
      recovered_model = model.__class__.from_config(model_config)
      if model.weights:
 
</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         _output = recovered_model.predict(input_data)
 </s> add         _output = recovered_model.predict(input_data, verbose=0) </s> remove     actual_output = model.predict(data)
 </s> add     actual_output = model.predict(data, verbose=0) </s> remove             "Train lightweight model for 1 iteration with WTL.",
 </s> add             "Train lightweight model for 1 iteration with WTL, AutoClip, MixedPrecion",
    actual_output = model.predict(input_data, verbose=0)
  
      # check with the functional API
      model = Model(inp, outp)
  
    actual_output = model.predict(input_data, verbose=0)
  
      # test serialization, weight setting at model level
      model_config = model.get_config()
      recovered_model = model.__class__.from_config(model_config)
      if model.weights:
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/nn_blocks_test.py