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 quantizer used to quantize the outputs from a layer.
  
  For certain layers, we may want to quantize the outputs tensors returned
  by the layer's `call` function. This allows us to quantize those output
  tensors.
  
  This function should return a list of quantizers. In most cases, a layer
  outputs only a single tensor so it should only have one quantizer. Return
  an empty list for if no quantization operation is desired on the results
  of the layer.
  
  Args:
    layer: layer being quantized.
  
  Returns:
    List of `Quantizer`s to be used to quantize the resulting tensors from
    a layer.
docstr_tok: ["Returns","the","quantizer","used","to","quantize","the","outputs","from","a","layer",".","For","certain","layers",",","we","may","want","to","quantize","the","outputs","tensors","returned","by","the","layer","'s","`","call","`","function",".","This","allows","us","to","quantize","those","output","tensors",".","This","function","should","return","a","list","of","quantizers",".","In","most","cases",",","a","layer","outputs","only","a","single","tensor","so","it","should","only","have","one","quantizer",".","Return","an","empty","list","for","if","no","quantization","operation","is","desired","on","the","results","of","the","layer",".","Args",":","layer",":","layer","being","quantized",".","Returns",":","List","of","`","Quantizer","`","s","to","be","used","to","quantize","the","resulting","tensors","from","a","layer","."]
code: def get_output_quantizers(self, layer):
      """Returns the quantizer used to quantize the outputs from a layer.
  
      For certain layers, we may want to quantize the outputs tensors returned
      by the layer's `call` function. This allows us to quantize those output
      tensors.
  
      This function should return a list of quantizers. In most cases, a layer
      outputs only a single tensor so it should only have one quantizer. Return
      an empty list for if no quantization operation is desired on the results
      of the layer.
  
      Args:
        layer: layer being quantized.
  
      Returns:
        List of `Quantizer`s to be used to quantize the resulting tensors from
        a layer.
      """
      raise NotImplementedError("Must be implemented in subclasses.")
  
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 5 for calling the QuantizeConfig obj's underlying member method code with example usage: obj.get_output_quantizers(layer) without return types