class_docstr: QuantizeConfig for keras.layers.Activation.
  
  `keras.layers.Activation` needs a separate `QuantizeConfig` since the
  decision to quantize depends on the specific activation type.
class_docstr_tok: ["QuantizeConfig","for","keras.layers.Activation",".","`","keras.layers.Activation","`","needs","a","separate","`","QuantizeConfig","`","since","the","decision","to","quantize","depends","on","the","specific","activation","type","."]
docstr: Construct a default QuantizeConfig for Activation layers.
  
  Args:
    quantize_output: Enable quantization of output, used to disable during
      transform.
docstr_tok: ["Construct","a","default","QuantizeConfig","for","Activation","layers",".","Args",":","quantize_output",":","Enable","quantization","of","output",",","used","to","disable","during","transform","."]
code: def __init__(self, quantize_output=True):
      """Construct a default QuantizeConfig for Activation layers.
  
      Args:
        quantize_output: Enable quantization of output, used to disable during
          transform.
      """
      self.quantize_output = quantize_output
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  from tensorflow_model_optimization.python.core.quantization.keras import quantize_config
  from tensorflow_model_optimization.python.core.quantization.keras import quantize_registry
  from tensorflow_model_optimization.python.core.quantization.keras import quantizers
  from tensorflow_model_optimization.python.core.quantization.keras.default_8bit import default_8bit_quantize_configs
  from tensorflow_model_optimization.python.core.quantization.keras.default_8bit import default_8bit_quantizers
label_desc: Use image node_id 1 to create a new Default8BitActivationQuantizeConfig object from inherited base classes: QuantizeConfig with example: obj = Default8BitActivationQuantizeConfig(quantize_output)