class_docstr: Apply (natural) log-transform to Y.
  
  This essentially means that we are model the observations as log-normally
  distributed. If `config` specifies `match_ci_width=True`, use a matching
  procedure based on the width of the CIs, otherwise (the default), use the
  delta method,
  
  Transform is applied only for the metrics specified in the transform config.
  Transform is done in-place.
code: def transform_optimization_config(
      self,
      optimization_config: OptimizationConfig,
      modelbridge: Optional[base_modelbridge.ModelBridge] = None,
      fixed_features: Optional[ObservationFeatures] = None,
  ) -> OptimizationConfig:
      for c in optimization_config.all_constraints:
          if c.metric.name in self.metric_names:
              base_str = f"LogY transform cannot be applied to metric {c.metric.name}"
              if c.relative:
                  raise ValueError(
                      f"{base_str} since it is subject to a relative constraint."
                  )
              elif c.bound <= 0:
                  raise ValueError(
                      f"{base_str} since the bound isn't positive, got: {c.bound}."
                  )
              else:
                  c.bound = np.log(c.bound)
      return optimization_config
  
imports: from __future__ import annotations
  from logging import Logger
  from typing import Callable, List, Optional, Tuple, TYPE_CHECKING
  import numpy
  from ax.core.observation import Observation, ObservationData, ObservationFeatures
  from ax.core.optimization_config import OptimizationConfig
  from ax.core.outcome_constraint import OutcomeConstraint
  from ax.core.search_space import SearchSpace
  from ax.modelbridge.transforms.base import Transform
  from ax.models.types import TConfig
  from ax.utils.common.logger import get_logger
  from scipy.stats import norm
label_desc: Use image node_id 2 for calling the LogY obj's underlying member method code with example usage: obj.transform_optimization_config(optimization_config, modelbridge, fixed_features) and returns: optimization_config