class_docstr: A metric defined by a dictionary mapping parameter values to the
  corresponding metric values.
  
  This provides an option to add normal noise with mean 0 and mean_sd scale
  to the given metric values.
class_docstr_tok: ["A","metric","defined","by","a","dictionary","mapping","parameter","values","to","the","corresponding","metric","values",".","This","provides","an","option","to","add","normal","noise","with","mean","0","and","mean_sd","scale","to","the","given","metric","values","."]
code: def clone(self) -> DictLookupMetric:
      return self.__class__(
          name=self._name,
          param_names=self.param_names,
          lookup_dict=self.lookup_dict,
          noise_sd=self.noise_sd,
          lower_is_better=self.lower_is_better,
      )
  
file_path: Ax/ax/metrics/dict_lookup.py
imports: from __future__ import annotations
  from typing import Any, Dict, List, Optional, Tuple, Union
  import numpy
  import pandas
  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 not_none
label_desc: Use image node_id 3 for calling the DictLookupMetric obj's underlying member method code with example usage: obj.clone() and returns: self