docstr: PCQAT training on the input model.
docstr_tok: ["PCQAT","training","on","the","input","model","."]
code: def _pcqat_training(
      self, preserve_sparsity, quant_aware_annotate_model
  ):
      """PCQAT training on the input model."""
      quant_aware_model = quantize.quantize_apply(
          quant_aware_annotate_model,
          scheme=default_8bit_cluster_preserve_quantize_scheme.Default8BitClusterPreserveQuantizeScheme(
              preserve_sparsity
          ),
      )
  
      self.compile_and_fit(quant_aware_model)
  
      stripped_pcqat_model = strip_clustering_cqat(quant_aware_model)
  
      # Check the unique weights of clustered_model and pcqat_model
      # layer 0 is the quantize_layer
      num_of_unique_weights_pcqat = self._get_number_of_unique_weights(
          stripped_pcqat_model, 1, "kernel"
      )
  
      sparsity_pcqat = self._get_sparsity(stripped_pcqat_model)
  
      return sparsity_pcqat, num_of_unique_weights_pcqat
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow
  from tensorflow_model_optimization.python.core.clustering.keras import cluster
  from tensorflow_model_optimization.python.core.clustering.keras import cluster_config
  from tensorflow_model_optimization.python.core.clustering.keras.experimental import cluster
  from tensorflow_model_optimization.python.core.quantization.keras import quantize
  from tensorflow_model_optimization.python.core.quantization.keras.collab_opts.cluster_preserve import default_8bit_cluster_preserve_quantize_scheme
  from tensorflow_model_optimization.python.core.quantization.keras.collab_opts.cluster_preserve.cluster_utils import strip_clustering_cqat
label_desc: Use image node_id 9 for calling the ClusterPreserveIntegrationTest obj's underlying member method code with example usage: obj._pcqat_training(preserve_sparsity, quant_aware_annotate_model) and returns: sparsity_pcqat,  num_of_unique_weights_pcqat