class_docstr: Default 8 bit PrunePreserveQuantizeRegistry.
class_docstr_tok: ["Default","8","bit","PrunePreserveQuantizeRegistry","."]
docstr: Returns the quantization config with addon sparsity.
  
  Args:
    layer: input layer to return quantize config for.
  
  Returns:
    Returns the quantization config with sparsity preserve weight_quantizer.
docstr_tok: ["Returns","the","quantization","config","with","addon","sparsity",".","Args",":","layer",":","input","layer","to","return","quantize","config","for",".","Returns",":","Returns","the","quantization","config","with","sparsity","preserve","weight_quantizer","."]
code: def get_quantize_config(self, layer):
      """Returns the quantization config with addon sparsity.
  
      Args:
        layer: input layer to return quantize config for.
  
      Returns:
        Returns the quantization config with sparsity preserve weight_quantizer.
      """
      quantize_config = default_8bit_quantize_registry.Default8BitQuantizeRegistry().get_quantize_config(
          layer
      )
      prune_aware_quantize_config = (
          self.apply_sparsity_preserve_quantize_config(
              layer, quantize_config
          )
      )
  
      return prune_aware_quantize_config
  
imports: import tensorflow
  from tensorflow_model_optimization.python.core.quantization.keras import quant_ops
  from tensorflow_model_optimization.python.core.quantization.keras import quantizers
  from tensorflow_model_optimization.python.core.quantization.keras.default_8bit import default_8bit_quantize_registry
  from tensorflow_model_optimization.python.core.quantization.keras.default_8bit import default_8bit_quantizers
label_desc: Use image node_id 1 for calling the Default8bitPrunePreserveQuantizeRegistry obj's underlying member method code with example usage: obj.get_quantize_config(layer) and returns: prune_aware_quantize_config