code: def get_observation2(
      first_metric_name: str = "a",
      second_metric_name: str = "b",
  ) -> Observation:
      return Observation(
          features=ObservationFeatures(
              parameters={"x": 3.0, "y": 2.0}, trial_index=1
          ),
          data=ObservationData(
              means=np.array([2.0, 1.0]),
              covariance=np.array([[2.0, 3.0], [4.0, 5.0]]),
              metric_names=[first_metric_name, second_metric_name],
          ),
          arm_name="1_1",
      )
  
imports: from logging import Logger
  from typing import Any, Dict, List, Optional, Type
  import numpy
  from ax.core.base_trial import TrialStatus
  from ax.core.experiment import Experiment
  from ax.core.observation import Observation, ObservationData, ObservationFeatures
  from ax.core.optimization_config import OptimizationConfig
  from ax.core.parameter import FixedParameter, RangeParameter
  from ax.core.search_space import SearchSpace
  from ax.modelbridge.base import ModelBridge
  from ax.modelbridge.dispatch_utils import choose_generation_strategy
  from ax.modelbridge.factory import get_sobol
  from ax.modelbridge.generation_node import GenerationNode
  from ax.modelbridge.generation_strategy import GenerationStep, GenerationStrategy
  from ax.modelbridge.model_spec import ModelSpec
  from ax.modelbridge.registry import Models
  from ax.modelbridge.transforms.base import Transform
  from ax.modelbridge.transforms.int_to_float import IntToFloat
  from ax.modelbridge.transition_criterion import MaxGenerationParallelism, MaxTrials, MinimumPreferenceOccurances, MinTrials
  from ax.models.torch.botorch_modular.surrogate import Surrogate
  from ax.utils.common.logger import get_logger
  from ax.utils.testing.core_stubs import get_experiment, get_search_space, get_search_space_for_value
  from botorch.acquisition.monte_carlo import qNoisyExpectedImprovement
  from botorch.models.fully_bayesian import SaasFullyBayesianSingleTaskGP
  from botorch.models.transforms.input import InputTransform, Normalize
  from botorch.models.transforms.outcome import OutcomeTransform, Standardize
label_desc: Use image node_id 7 for calling a global function with example usage: get_observation2(first_metric_name, second_metric_name) and returns: Observation