code: def _write_dependency_reply(model_dir):
      replay_string = """
        minimum_depth: 2
        maximum_depth: 20
        problem_type: CNN
        search_type: ADAPTIVE_COORDINATE_DESCENT
        replay {
          towers {
            architecture: "RNN_128"
            architecture: "RNN_128"
            hparams {
              hparam {
                key: "learning_rate"
                value {
                  float_value: 0.021
                }
              }
              hparam {
                key: "initial_architecture"
                value {
                  bytes_list {
                    value: "RNN_256"
                    value: "RNN_128"
                  }
                }
              }
            }
          }
        }
      """
      dependency_file = os.path.join(
          model_dir, "previous_dirs_dependencies.txt"
      )
      with tf.io.gfile.GFile(dependency_file, "w") as f:
          f.write(os.path.join(model_dir, "1"))
      tf.io.gfile.makedirs(os.path.join(model_dir, "1"))
      with tf.io.gfile.GFile(
          os.path.join(model_dir, "1", "replay_config.pbtxt"), "w"
      ) as f:
          f.write(replay_string)
  
imports: import functools
  import os
  from absl.testing import absltest
  from absl.testing import parameterized
  import mock
  from model_search import hparam
  from model_search.architecture import architecture_utils
  from model_search.generators import trial_utils
  from model_search.proto import distillation_spec_pb2
  from model_search.proto import ensembling_spec_pb2
  from model_search.proto import phoenix_spec_pb2
  import tensorflow.compat.v2
  from google.protobuf import text_format
label_desc: Use image node_id 3 for calling a global function with example usage: _write_dependency_reply(model_dir) without return types