class_docstr: A metric that trains and evaluates an sklearn model.
  
  The evaluation metric is the k-fold "score". The scoring function
  depends on the model type and task type (e.g. classification/regression),
  but higher scores are better.
  
  See sklearn documentation for supported parameters.
  
  In addition, this metric supports tuning the hidden_layer_size
  and the number of hidden layers (num_hidden_layers) of a NN model.
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code: def clone(self) -> SklearnMetric:
      return self.__class__(
          name=self._name,
          lower_is_better=checked_cast(bool, self.lower_is_better),
          model_type=self.model_type,
          dataset=self.dataset,
      )
  
imports: from __future__ import annotations
  from copy import deepcopy
  from enum import Enum
  from functools import lru_cache
  from math import sqrt
  from typing import Any, Dict, Tuple
  import numpy
  import pandas
  from ax.core.arm import Arm
  from ax.core.base_trial import BaseTrial
  from ax.core.data import Data
  from ax.core.metric import Metric, MetricFetchE, MetricFetchResult
  from ax.utils.common.result import Err, Ok
  from ax.utils.common.typeutils import checked_cast
  from sklearn import datasets
  from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
  from sklearn.model_selection import cross_val_score
  from sklearn.neural_network import MLPClassifier, MLPRegressor
label_desc: Use image node_id 2 for calling the SklearnMetric obj's underlying member method code with example usage: obj.clone() and returns: self