class_docstr: This class provides a special lookup function for the the weights 'w'.
  
  It reshapes and tile centroids the same way as the weights. This allows us
  to find pulling indices efficiently.
code: def get_pulling_indices(self, weight):
      clst_num = self.cluster_centroids.shape[0]
      tiled_weights = tf.tile(
          tf.expand_dims(weight, axis=2), [1, 1, clst_num]
      )
      tiled_cluster_centroids = tf.tile(
          tf.reshape(self.cluster_centroids, [1, 1, clst_num]),
          [weight.shape[0], weight.shape[1], 1],
      )
  
      # We find the nearest cluster centroids and store them so that ops can build
      # their kernels upon it
      pulling_indices = tf.argmin(
          tf.abs(tiled_weights - tiled_cluster_centroids), axis=2
      )
  
      return pulling_indices
  
imports: 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 import clusterable_layer
  from tensorflow_model_optimization.python.core.clustering.keras import clustering_algorithm
label_desc: Use image node_id 1 for calling the ClusterableWeightsCA obj's underlying member method code with example usage: obj.get_pulling_indices(weight) and returns: pulling_indices