class_docstr: Estimator wrapper to add reporting to metadata storage after evaluation.
class_docstr_tok: ["Estimator","wrapper","to","add","reporting","to","metadata","storage","after","evaluation","."]
code: def __init__(
      self,
      model_fn,
      model_dir=None,
      config=None,
      params=None,
      warm_start_from=None,
      metadata=None,
  ):
      tf.estimator.Estimator._assert_members_are_not_overridden = (
          staticmethod(  # pylint: disable=protected-access
              lambda _: None
          )
      )
      super(Estimator, self).__init__(
          model_fn=model_fn,
          config=config,
          params=params,
          warm_start_from=warm_start_from,
      )
      self._metadata = metadata
      self._model_dir = config.model_dir
  
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 1 to create a new Estimator object from inherited base classes: tf.estimator with example: obj = Estimator(model_fn, model_dir, config, params, warm_start_from, metadata)