class_docstr: Quantizes the weights and activations of the keras layer it wraps.
class_docstr_tok: ["Quantizes","the","weights","and","activations","of","the","keras","layer","it","wraps","."]
code: def from_config(cls, config):
      config = config.copy()
  
      # QuantizeWrapper may be constructed with any QuantizeConfig and the
      # wrapper itself cannot know all the possible config classes.
      # The deserialization code should ensure the QuantizeConfig is in keras
      # serialization scope.
      quantize_config = deserialize_keras_object(
          config.pop("quantize_config"),
          module_objects=globals(),
          custom_objects=None,
      )
  
      layer_config = config.pop("layer")
      use_legacy_format = "module" not in layer_config
  
      layer = quantize_utils.deserialize_layer(
          layer_config, use_legacy_format=use_legacy_format
      )
  
      return cls(layer=layer, quantize_config=quantize_config, **config)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  from tensorflow.python.util import tf_inspect
  from tensorflow_model_optimization.python.core.keras import metrics
  from tensorflow_model_optimization.python.core.keras import utils
  from tensorflow_model_optimization.python.core.quantization.keras import quantize_aware_activation
  from tensorflow_model_optimization.python.core.quantization.keras import utils
label_desc: Use image node_id 9 for calling the QuantizeWrapper obj's underlying member method code with example usage: obj.from_config(cls, config) and returns: cls