code: def _suggest_and_create_architecture(
      create_new_architecture_fn,
      relevant_trials,
      hparams,
      my_id,
      run_config,
      search_algorithm,
      phoenix_spec,
      input_layer_fn,
      is_training,
      logits_dimension,
      dropout_rate,
      aux,
  ):
      """A function to suggest and create an archiecture.
  
      Args:
        create_new_architecture_fn: A function to create the architecture with the
          following signature  Input architecture, A list of block encoding the
          architecture. prev_trial, A trial if mutating a previous trial, otherwise
          None. input_tensor, A tensor with the input. lengths, A tensor with the
          lengths or None if non-sequential.  Output A list of
          architecture_utils.TowerSpec, and a list of their architectures
        relevant_trials: A list of past evaluated trials.
        hparams: The hparams object for the model.
        my_id: The currect trial id.
        run_config: A tf.estimator.RunConfig for the training.
        search_algorithm: A model_search.search.SearchAlgorithm
        phoenix_spec: A PhoenixSpec proto.
        input_layer_fn: A function with the following signature,  Input features, A
          dict of the features. is_training, A boolean indicating if we are in
          training mode. scope_name, A string with the scope name,
          lengths_feature_name, A string holding the lengths feature name for
          sequential problems, otherwise None.  Output Two tensors. One with the
          input, and one for lengths for sequential problems (None if
          non-sequential).
        is_training: A boolean indicating if we are in training mode.
        logits_dimension: An int specifying the cardinality of the logits.
        dropout_rate: The dropout rate between blocks.
        aux: A boolean indicating if we allow auxiliry head.
  
      Returns:
        A list of architecture_utils.TowerSpec, and a list of their architectures
      """
      architecture = None
      input_tensor = None
      lengths = None
  
      architecture, prev_trial = search_algorithm.get_suggestion(
          relevant_trials, hparams, my_id, run_config.model_dir
      )
      return create_new_architecture_fn(
          architecture=architecture, prev_trial=prev_trial
      )
  
imports: import functools
  from absl import logging
  from model_search import block_builder
  from model_search import search
  from model_search.architecture import architecture_utils
  from model_search.architecture import tower
  from model_search.generators import base_tower_generator
  from model_search.generators import trial_utils
  from model_search.proto import phoenix_spec_pb2
  from model_search.proto import transfer_learning_spec_pb2
  import numpy
  import tensorflow.compat.v2
label_desc: Use image node_id 1 for calling a global function with example usage: _suggest_and_create_architecture(create_new_architecture_fn, relevant_trials, hparams, my_id, run_config, search_algorithm, phoenix_spec, input_layer_fn, is_training, logits_dimension, dropout_rate, aux) and returns: create_new_architecture_fn