class_docstr: Generates candidates towers for Phoenix via search algorithms.
class_docstr_tok: ["Generates","candidates","towers","for","Phoenix","via","search","algorithms","."]
code: def _get_user_suggestion(self, trial_id):
      suggestion = trial_id - 1
      architecture = self._phoenix_spec.user_suggestions[
          suggestion
      ].architecture
      architecture = [
          block_builder.BlockType[block_type]
          for block_type in architecture
      ]
      return np.array(
          architecture_utils.fix_architecture_order(
              architecture, self._phoenix_spec.problem_type
          )
      )
  
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 5 for calling the SearchCandidateGenerator obj's underlying member method code with example usage: obj._get_user_suggestion(trial_id) and returns: np