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 set_quantize_activations(self, layer, quantize_activations):
      self._assert_activation_layer(layer)
  
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 6 for calling the DefaultNBitActivationQuantizeConfig obj's underlying member method code with example usage: obj.set_quantize_activations(layer, quantize_activations) without return types