class_docstr: Phoenix: A smart search AutoML algorithm.
class_docstr_tok: ["Phoenix",":","A","smart","search","AutoML","algorithm","."]
docstr: Returns a Phoenix `Estimator` for train and evaluation.
  
  Args:
    run_config: `RunConfig` object to configure the runtime settings.
    hparams: `HParams` instance defining custom hyperparameters.
    train_steps: The total number of training steps.
  
  Returns:
    Returns an `Estimator`.
  
  Raises:
    ValueError: in case flatten is used as a search block or is missing from
    the initial architecture.
docstr_tok: ["Returns","a","Phoenix","`","Estimator","`","for","train","and","evaluation",".","Args",":","run_config",":","`","RunConfig","`","object","to","configure","the","runtime","settings",".","hparams",":","`","HParams","`","instance","defining","custom","hyperparameters",".","train_steps",":","The","total","number","of","training","steps",".","Returns",":","Returns","an","`","Estimator","`",".","Raises",":","ValueError",":","in","case","flatten","is","used","as","a","search","block","or","is","missing","from","the","initial","architecture","."]
code: def get_estimator(self, run_config, hparams, train_steps):
      """Returns a Phoenix `Estimator` for train and evaluation.
  
      Args:
        run_config: `RunConfig` object to configure the runtime settings.
        hparams: `HParams` instance defining custom hyperparameters.
        train_steps: The total number of training steps.
  
      Returns:
        Returns an `Estimator`.
  
      Raises:
        ValueError: in case flatten is used as a search block or is missing from
        the initial architecture.
      """
  
      if not all(
          "FLATTEN" not in block for block in hparams.new_block_type
      ):
          raise ValueError("Flatten cannot be a search block type")
  
      return Estimator(
          model_fn=self._make_model_fn(
              run_config=run_config,
              train_steps=train_steps,
              use_tpu=False,
          ),
          config=run_config,
          params=hparams,
          metadata=self._metadata,
      )
  
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 7 for calling the Phoenix obj's underlying member method code with example usage: obj.get_estimator(run_config, hparams, train_steps) and returns: Estimator