tensorflow_model_optimization/python/core/quantization/keras/default_8bit/default_8bit_quantize_registry.py [312:342]:
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  def get_weights_and_quantizers(self, layer):
    return [(getattr(layer, weight_attr), self.weight_quantizer)
            for weight_attr in self.weight_attrs]

  def get_activations_and_quantizers(self, layer):
    return [(getattr(layer, activation_attr), self.activation_quantizer)
            for activation_attr in self.activation_attrs]

  def set_quantize_weights(self, layer, quantize_weights):
    if len(self.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(self.weight_attrs)))

    for weight_attr, weight in zip(self.weight_attrs, quantize_weights):
      current_weight = getattr(layer, weight_attr)
      if current_weight.shape != weight.shape:
        raise ValueError('Existing layer weight shape {} is incompatible with'
                         'provided weight shape {}'.format(
                             current_weight.shape, weight.shape))

      setattr(layer, weight_attr, weight)

  def set_quantize_activations(self, layer, quantize_activations):
    if len(self.activation_attrs) != len(quantize_activations):
      raise ValueError(
          '`set_quantize_activations` called on layer {} with {} '
          'activation parameters, but layer expects {} values.'.format(
              layer.name, len(quantize_activations),
              len(self.activation_attrs)))
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tensorflow_model_optimization/python/core/quantization/keras/experimental/default_n_bit/default_n_bit_quantize_registry.py [350:380]:
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  def get_weights_and_quantizers(self, layer):
    return [(getattr(layer, weight_attr), self.weight_quantizer)
            for weight_attr in self.weight_attrs]

  def get_activations_and_quantizers(self, layer):
    return [(getattr(layer, activation_attr), self.activation_quantizer)
            for activation_attr in self.activation_attrs]

  def set_quantize_weights(self, layer, quantize_weights):
    if len(self.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(self.weight_attrs)))

    for weight_attr, weight in zip(self.weight_attrs, quantize_weights):
      current_weight = getattr(layer, weight_attr)
      if current_weight.shape != weight.shape:
        raise ValueError('Existing layer weight shape {} is incompatible with'
                         'provided weight shape {}'.format(
                             current_weight.shape, weight.shape))

      setattr(layer, weight_attr, weight)

  def set_quantize_activations(self, layer, quantize_activations):
    if len(self.activation_attrs) != len(quantize_activations):
      raise ValueError(
          '`set_quantize_activations` called on layer {} with {} '
          'activation parameters, but layer expects {} values.'.format(
              layer.name, len(quantize_activations),
              len(self.activation_attrs)))
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