Error in energy estimation for AveragePooling2D layers

Greetings, I am trying to quantize the network for the KWS application using DS CNN. The network is described here ([LINK](https://github.com/mlcommons/tiny/blob/master/benchmark/training/keyword_spotting/keras_model.py))(lines from 85 to 141).

When running AutoQKeras, It shows an error on energy estimation for Average2D pooling layers:

Traceback (most recent call last):
File "/home/auto_qk.py", line 180, in
autoqk = AutoQKeras(model, metrics=[keras.metrics.SparseCategoricalAccuracy()], custom_objects=custom_objects, **run_config)
File "/usr/local/lib/python3.8/dist-packages/qkeras/autoqkeras/autoqkeras_internal.py", line 831, in __init__
self.hypermodel = AutoQKHyperModel(
File "/usr/local/lib/python3.8/dist-packages/qkeras/autoqkeras/autoqkeras_internal.py", line 125, in __init__
self.reference_size = self.target.get_reference(model)
File "/usr/local/lib/python3.8/dist-packages/qkeras/autoqkeras/forgiving_metrics/forgiving_energy.py", line 121, in get_reference
energy_dict = q.pe(
File "/usr/local/lib/python3.8/dist-packages/qkeras/qtools/run_qtools.py", line 85, in pe
energy_dict = qenergy.energy_estimate(
File "/usr/local/lib/python3.8/dist-packages/qkeras/qtools/qenergy/qenergy.py", line 302, in energy_estimate
add_energy = OP[get_op_type(accumulator.output)]["add"](
AttributeError: 'NoneType' object has no attribute 'output'

When I remove the Average2D pooling layer, the AutoQKeras does not produce the error. I tried to set quant parameters for AveragePooling, but no luck.

Code for AutoQKeras:

### AutoQkeras start

# set quantization configs

quantization_config = {
"kernel": {
"binary": 1,
"stochastic_binary": 1,
"ternary": 2,
"stochastic_ternary": 2,
"quantized_bits(2,0,1,1,alpha=\"auto_po2\")": 2,
"quantized_bits(3,0,1,1,alpha=\"auto_po2\")": 3,
"quantized_bits(4,0,1,1,alpha=\"auto_po2\")": 4,
"quantized_bits(3,0,1,1,alpha=\"auto_po2\")": 5,
"quantized_bits(4,0,1,1,alpha=\"auto_po2\")": 6
},
"bias": {
"quantized_bits(2,0,1,1,alpha=\"auto_po2\")": 2,
"quantized_bits(3,0,1,1,alpha=\"auto_po2\")": 3,
"quantized_bits(4,0,1,1,alpha=\"auto_po2\")": 4,
"quantized_bits(3,0,1,1,alpha=\"auto_po2\")": 5,
"quantized_bits(4,0,1,1,alpha=\"auto_po2\")": 6
},
"activation": {
"binary": 1,
"ternary": 2,
"quantized_bits(2,0,1,1,alpha=\"auto_po2\")": 2,
"quantized_bits(3,0,1,1,alpha=\"auto_po2\")": 3,
"quantized_bits(4,0,1,1,alpha=\"auto_po2\")": 4,
"quantized_bits(3,0,1,1,alpha=\"auto_po2\")": 5,
"quantized_bits(4,0,1,1,alpha=\"auto_po2\")": 6
},
"linear": {
"binary": 1,
"ternary": 2,
"quantized_bits(2,0,1,1,alpha=\"auto_po2\")": 2,
"quantized_bits(3,0,1,1,alpha=\"auto_po2\")": 3,
"quantized_bits(4,0,1,1,alpha=\"auto_po2\")": 4,
"quantized_bits(3,0,1,1,alpha=\"auto_po2\")": 5,
"quantized_bits(4,0,1,1,alpha=\"auto_po2\")": 6
}
}

# define limits
limit = {
"Dense": [4, 4, 4],
"Conv2D": [4, 4, 4],
"DepthwiseConv2D": [4, 4, 4],
"Activation": [4],
"AveragePooling2D": [4, 4, 4],
"BatchNormalization": [],
"Dense":[],
}

# define goal (delta = forgiving factor lets put at 8% like in tutorial )

goal = {
"type": "energy",
"params": {
"delta_p": 8.0,
"delta_n": 8.0,
"rate": 2.0,
"stress": 1.0,
"process": "horowitz",
"parameters_on_memory": ["sram", "sram"],
"activations_on_memory": ["sram", "sram"],
"rd_wr_on_io": [False, False],
"min_sram_size": [0, 0],
"source_quantizers": ["int8"],
"reference_internal": "int8",
"reference_accumulator": "int32"
}
}

# SOME RUN CONFIGS

run_config = {
"output_dir": Flags.bg_path + "auto_qk_dump",
"goal": goal,
"quantization_config": quantization_config,
"learning_rate_optimizer": False,
"transfer_weights": False,
"mode": "random",
"seed": 42,
"limit": limit,
"tune_filters": "layer",
"tune_filters_exceptions": "^dense",
# first layer is input, layer two layers are softmax and flatten
"layer_indexes": range(1, len(model.layers)-1),
"max_trials": 20
}

# Start autoQkeras

model.summary()
model.compile(
#optimizer=keras.optimizers.RMSprop(learning_rate=args.learning_rate), # Optimizer
optimizer=keras.optimizers.Adam(learning_rate=Flags.learning_rate), # Optimizer
# Loss function to minimize
loss=keras.losses.SparseCategoricalCrossentropy(),
# List of metrics to monitor
metrics=[keras.metrics.SparseCategoricalAccuracy()],
)
#model = keras.models.load_model(Flags.saved_model_path)

custom_objects = {}
autoqk = AutoQKeras(model, metrics=[keras.metrics.SparseCategoricalAccuracy()], custom_objects=custom_objects, **run_config)
autoqk.fit(ds_train, validation_data=ds_val, epochs=Flags.epochs, callbacks=callbacks)

qmodel = autoqk.get_best_model()
model.save_weights(Flags.bg_path + "auto_qk_dump/","qmodel.h5")
### AutoQkeras stop