class_docstr: If an observation data contains multiple observations of a metric, they
  are combined using inverse variance weighting.
class_docstr_tok: ["If","an","observation","data","contains","multiple","observations","of","a","metric",",","they","are","combined","using","inverse","variance","weighting","."]
code: def _transform_observation_data(
      self,
      observation_data: List[ObservationData],
  ) -> List[ObservationData]:
      # pyre: conflicting_noiseless is declared to have type `str` but is
      # pyre-fixme[9]: used as type `typing.Union[float, int, str]`.
      conflicting_noiseless: str = self.config.get(
          "conflicting_noiseless", "warn"
      )
      return [
          ivw_metric_merge(
              obsd=obsd, conflicting_noiseless=conflicting_noiseless
          )
          for obsd in observation_data
      ]
  
file_path: Ax/ax/modelbridge/transforms/ivw.py
imports: from logging import Logger
  from typing import Dict, List
  import numpy
  from ax.core.observation import ObservationData
  from ax.modelbridge.transforms.base import Transform
  from ax.utils.common.logger import get_logger
label_desc: Use image node_id 1 for calling the IVW obj's underlying member method code with example usage: obj._transform_observation_data(observation_data) and returns: unknown