class_docstr: Default 8 bit ClusterPreserveQuantizeRegistry.
docstr: Returns the quantization config with weight_quantizer for a given layer.
  
  Args:
    layer: input layer to return quantize config for.
  Returns:
    Returns the quantization config for cluster preserve weight_quantizer.
code: def get_quantize_config(self, layer):
      """Returns the quantization config with weight_quantizer for a given layer.
  
      Args:
        layer: input layer to return quantize config for.
      Returns:
        Returns the quantization config for cluster preserve weight_quantizer.
      """
      quantize_config = default_8bit_quantize_registry.Default8BitQuantizeRegistry().get_quantize_config(
          layer
      )
      cluster_aware_quantize_config = super(
          Default8bitClusterPreserveQuantizeRegistry, self
      ).apply_cluster_preserve_quantize_config(layer, quantize_config)
  
      return cluster_aware_quantize_config
  
imports: import logging
  import warnings
  import tensorflow
  from tensorflow_model_optimization.python.core.clustering.keras import cluster_config
  from tensorflow_model_optimization.python.core.clustering.keras import clustering_registry
  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 Default8bitClusterPreserveQuantizeRegistry obj's underlying member method code with example usage: obj.get_quantize_config(layer) and returns: cluster_aware_quantize_config