code: def _merge_hparams(original_hparams, overrides):
      """Merges to hp.HParams objects."""
      # make a copy
      hparams = hp.HParams(**original_hparams.values())
      existing_ones = {
          k: v for k, v in overrides.values().items() if k in hparams
      }
      new_ones = {
          k: v
          for k, v in overrides.values().items()
          if k not in hparams
      }
      hparams.override_from_dict(existing_ones)
      for k, v in new_ones.items():
          hparams.add_hparam(k, v)
      return hparams
  
imports: import functools
  import inspect
  from absl import logging
  import keras_tuner
  from model_search import controller
  from model_search import hparam
  from model_search import loss_fns
  from model_search import metric_fns
  from model_search import task_manager
  from model_search.architecture import architecture_utils
  from model_search.generators import base_tower_generator
  from model_search.generators import trial_utils
  from model_search.meta import transfer_learning
  from model_search.metadata import ml_metadata_db
  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 2 for calling a global function with example usage: _merge_hparams(original_hparams, overrides) and returns: hparams