class_docstr: QuantizeConfig for non recurrent Keras layers.
class_docstr_tok: ["QuantizeConfig","for","non","recurrent","Keras","layers","."]
code: def set_quantize_activations(self, layer, quantize_activations):
      if len(self.activation_attrs) != len(quantize_activations):
          raise ValueError(
              "`set_quantize_activations` called on layer {} with {} "
              "activation parameters, but layer expects {} values.".format(
                  layer.name,
                  len(quantize_activations),
                  len(self.activation_attrs),
              )
          )
  
      for activation_attr, activation in zip(
          self.activation_attrs, quantize_activations
      ):
          setattr(layer, activation_attr, activation)
  
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 5 for calling the Default8BitQuantizeConfig obj's underlying member method code with example usage: obj.set_quantize_activations(layer, quantize_activations) without return types