class_docstr: Quantize weights while preserving clusters.
docstr: Build (P)CQAT wrapper.
  
  When preserve_sparsity is true and the input is clustered.
  
  Args:
    tensor_shape: Shape of weights which needs to be quantized.
    name: Name of weights in layer.
    layer: Quantization wrapped keras layer.
  Returns:
    Dictionary of centroids, indices and
    quantization params, the dictionary will be passed
    to __call__ function.
code: def build(self, tensor_shape, name, layer):
      """Build (P)CQAT wrapper.
  
      When preserve_sparsity is true and the input is clustered.
  
      Args:
        tensor_shape: Shape of weights which needs to be quantized.
        name: Name of weights in layer.
        layer: Quantization wrapped keras layer.
      Returns:
        Dictionary of centroids, indices and
        quantization params, the dictionary will be passed
        to __call__ function.
      """
      # To get all the initial values from pretrained clustered model
      result = self._build_clusters(name, layer)
      # Result can have clustering nodes, then this is CQAT
      # Result can have both clustering nodes and sparsity mask, then
      # this will be PCQAT
      result.update(
          super(ClusterPreserveDefaultWeightsQuantizer, self).build(
              tensor_shape, name, layer
          )
      )
  
      return result
  
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 3 for calling the ClusterPreserveDefaultWeightsQuantizer obj's underlying member method code with example usage: obj.build(tensor_shape, name, layer) and returns: result