class_docstr: ABC interface for Keras layers to express how they should be quantized.
  
  This is an experimental API not subject to backward compatibility.
  
  QuantizeConfig encapsulates all the information needed by the quantization
  code to quantize a layer. It specifies what parts of a layer should be
  quantized and how they should be quantized.
  
  It can be used to precisely control the quantization behavior of a layer.
  The framework provides default behavior for each layer, but this can be used
  to override it.
  
  Create QuantizeConfig for a Dense layer:
  
  ```python
  class MyDenseQuantizeConfig(QuantizeConfig):
  
    def get_weights_and_quantizers(self, layer):
      return [(layer.kernel, LastValueQuantizer())]
  
    def get_activations_and_quantizers(self, layer):
      return [(layer.activation, MovingAverageQuantizer())]
  
    def set_quantize_weights(self, layer, quantize_weights):
      layer.kernel = quantize_weights[0]
  
    def set_quantize_activations(self, layer, quantize_activations):
      layer.activation = quantize_activations[0]
  
    def get_output_quantizers(self, layer):
      # Does not quantize output, since we return an empty list.
      return []
  
    def get_config(self):
      return {}
  ```
  
  For a full example, see
  https://www.tensorflow.org/model_optimization/guide/quantization/training_comprehensive_guide.md
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docstr: Returns the config used to serialize `QuantizeConfig`.
docstr_tok: ["Returns","the","config","used","to","serialize","`","QuantizeConfig","`","."]
code: def get_config(self):
      """Returns the config used to serialize `QuantizeConfig`."""
      raise NotImplementedError(
          "QuantizeConfig should implement get_config()."
      )
  
imports: import abc
  import six
  from tensorflow_model_optimization.python.core.quantization.keras import quantizers
  from tensorflow_model_optimization.python.core.quantization.keras import utils
label_desc: Use image node_id 6 for calling the QuantizeConfig obj's underlying member method code with example usage: obj.get_config() without return types