tensorflow_model_optimization/python/core/quantization/keras/default_8bit/default_8bit_quantize_registry.py [406:427]:
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      for activation_attr in activation_attrs_cell:
        activations_quantizers.append(
            (getattr(rnn_cell, activation_attr), self.activation_quantizer))

    return activations_quantizers

  def _flatten(self, list_of_lists):
    flat_list = []
    for sublist in list_of_lists:
      for item in sublist:
        flat_list.append(item)
    return flat_list

  def set_quantize_weights(self, layer, quantize_weights):
    flattened_weight_attrs = self._flatten(self.weight_attrs)
    if len(flattened_weight_attrs) != len(quantize_weights):
      raise ValueError(
          '`set_quantize_weights` called on layer {} with {} '
          'weight parameters, but layer expects {} values.'.format(
              layer.name, len(quantize_weights), len(flattened_weight_attrs)))

    i = 0
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tensorflow_model_optimization/python/core/quantization/keras/experimental/default_n_bit/default_n_bit_quantize_registry.py [447:468]:
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      for activation_attr in activation_attrs_cell:
        activations_quantizers.append(
            (getattr(rnn_cell, activation_attr), self.activation_quantizer))

    return activations_quantizers

  def _flatten(self, list_of_lists):
    flat_list = []
    for sublist in list_of_lists:
      for item in sublist:
        flat_list.append(item)
    return flat_list

  def set_quantize_weights(self, layer, quantize_weights):
    flattened_weight_attrs = self._flatten(self.weight_attrs)
    if len(flattened_weight_attrs) != len(quantize_weights):
      raise ValueError(
          '`set_quantize_weights` called on layer {} with {} '
          'weight parameters, but layer expects {} values.'.format(
              layer.name, len(quantize_weights), len(flattened_weight_attrs)))

    i = 0
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