class_docstr: Base class for H2O Estimators.
  
  H2O Estimators implement the following methods for model construction:
  
      - ``start()`` - Top-level user-facing API for asynchronous model build
      - ``join()``  - Top-level user-facing API for blocking on async model build
      - ``train()`` - Top-level user-facing API for model building.
      - ``fit()`` - Used by scikit-learn.
  
  Because H2OEstimator instances are instances of ModelBase, these objects can use the H2O model API.
code: def _metrics_class(model_json):
      model_type = model_json["output"]["model_category"]
      valid_metrics_class = None
      if model_type == "Binomial":
          metrics_class = H2OBinomialModelMetrics
          model_class = H2OBinomialModel
      elif model_type == "BinomialUplift":
          metrics_class = H2OBinomialUpliftModelMetrics
          model_class = H2OBinomialUpliftModel
      elif model_type == "Clustering":
          metrics_class = H2OClusteringModelMetrics
          model_class = H2OClusteringModel
      elif model_type == "Regression":
          metrics_class = H2ORegressionModelMetrics
          model_class = H2ORegressionModel
      elif model_type == "Multinomial":
          metrics_class = H2OMultinomialModelMetrics
          model_class = H2OMultinomialModel
      elif model_type == "Ordinal":
          metrics_class = H2OOrdinalModelMetrics
          model_class = H2OOrdinalModel
      elif model_type == "AutoEncoder":
          metrics_class = H2ODefaultModelMetrics
          model_class = H2OAutoEncoderModel
      elif model_type == "DimReduction":
          metrics_class = H2ODimReductionModelMetrics
          model_class = H2ODimReductionModel
      elif model_type == "WordEmbedding":
          metrics_class = H2ODefaultModelMetrics
          model_class = H2OWordEmbeddingModel
      elif model_type == "AnomalyDetection":
          metrics_class = H2OAnomalyDetectionModelMetrics
          valid_metrics_class = H2OBinomialModelMetrics
          model_class = H2OAnomalyDetectionModel
      elif model_type == "CoxPH":
          metrics_class = H2ORegressionCoxPHModelMetrics
          model_class = (
              H2OCoxPHModel
              if model_json["algo"] != "generic"
              else H2OCoxPHMojoModel
          )
      elif model_type == "TargetEncoder":
          metrics_class = H2ODefaultModelMetrics
          model_class = None
      elif model_type == "Unknown":
          metrics_class = None
          model_class = None
      else:
          raise NotImplementedError(model_type)
      if valid_metrics_class is None:
          valid_metrics_class = metrics_class
      return [metrics_class, model_class, valid_metrics_class]
  
imports: from h2o.utils.compatibility import *
  from datetime import datetime
  import inspect
  import warnings
  import h2o
  from h2o.base import Keyed
  from h2o.exceptions import H2OValueError, H2OResponseError
  from h2o.frame import H2OFrame
  from h2o.job import H2OJob
  from h2o.utils.mixin import assign, load_ext, mixin
  from h2o.utils.shared_utils import quoted, LookupSeq
  from h2o.utils.typechecks import assert_is_type, is_type, numeric, FunctionType
  from h2o.model import ModelBase, H2OSegmentModels
  from h2o.model.models import *
  from h2o.model.metrics import *
label_desc: Use image node_id 16 for calling the H2OEstimator obj's underlying member method code with example usage: obj._metrics_class(model_json) and returns: list