code: def kbs_config(embedding_dimension):
      """Returns the configuration to use dynamic embedding neighbor cache."""
      config = de_config_pb2.DynamicEmbeddingConfig()
      text_format.Parse(
          """
      embedding_dimension: %d
      knowledge_bank_config {
        initializer {
          random_uniform_initializer {
            low: -0.5
            high: 0.5
          }
          use_deterministic_seed: true
        }
        extension {
          [type.googleapis.com/carls.InProtoKnowledgeBankConfig] {}
        }
      }
    """
          % embedding_dimension,
          config,
      )
      return config
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import threading
  from absl import app
  from absl import flags
  from absl import logging
  import attr
  import neural_structured_learning
  from research.carls import dynamic_embedding_config_pb2
  from research.carls import dynamic_embedding_neighbor_cache
  from research.carls import graph_regularization
  from research.carls import kbs_server_helper_pybind
  import tensorflow
  from google.protobuf import text_format
label_desc: Use image node_id 9 for calling a global function with example usage: kbs_config(embedding_dimension) and returns: config