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","."]
code: def get_output_quantizers(self, layer):
      self._assert_activation_layer(layer)
  
      if not hasattr(layer.activation, "__name__"):
          raise ValueError(
              "Activation {} not supported by "
              "DefaultNBitActivationQuantizeConfig.".format(
                  layer.activation
              )
          )
  
      if layer.activation.__name__ in ["relu", "relu6", "swish"]:
          # 'relu' should generally get fused into the previous layer.
          return [
              quantizers.MovingAverageQuantizer(
                  num_bits=self._num_bits_activation,
                  per_axis=False,
                  symmetric=False,
                  narrow_range=False,
              )
          ]  # activation/output
      elif layer.activation.__name__ in [
          "linear",
          "softmax",
          "sigmoid",
          "tanh",
      ]:
          return []
  
      raise ValueError(
          "Activation {} not supported by "
          "DefaultNBitActivationQuantizeConfig.".format(
              layer.activation
          )
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from typing import Any, Dict
  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.experimental.default_n_bit import default_n_bit_quantize_configs
  from tensorflow_model_optimization.python.core.quantization.keras.experimental.default_n_bit import default_n_bit_quantizers
label_desc: Use image node_id 7 for calling the DefaultNBitActivationQuantizeConfig obj's underlying member method code with example usage: obj.get_output_quantizers(layer) and returns: list, list