code: def _filter_feasible_rows(
      df: pd.DataFrame,
      optimization_config: OptimizationConfig,
  ) -> pd.DataFrame:
      """Filter out arms that do not satisfy outcome constraints
  
      Looks at all arm data collected and removes rows corresponding to arms in
      which one or more of their associated metrics' 95% confidence interval
      falls outside of any outcome constraint's bounds (i.e. we are 95% sure the
      bound is not satisfied).
      """
  
      feasible = df.loc[
          _is_row_feasible(
              df=df, optimization_config=optimization_config
          )
      ]
      if feasible.empty:
          raise ValueError(
              "No points satisfied all outcome constraints within 95 percent"
              "confidence interval."
          )
  
      return feasible
  
imports: from collections import OrderedDict
  from functools import reduce
  from logging import Logger
  from typing import Dict, Iterable, List, Optional, Tuple, Type
  import pandas
  import torch
  from ax.core.base_trial import TrialStatus
  from ax.core.batch_trial import BatchTrial
  from ax.core.data import Data
  from ax.core.experiment import Experiment
  from ax.core.generator_run import GeneratorRun
  from ax.core.objective import MultiObjective, Objective, ScalarizedObjective
  from ax.core.observation import Observation
  from ax.core.optimization_config import MultiObjectiveOptimizationConfig, OptimizationConfig
  from ax.core.outcome_constraint import ObjectiveThreshold, OutcomeConstraint
  from ax.core.trial import Trial
  from ax.core.types import ComparisonOp, TModelPredictArm, TParameterization
  from ax.exceptions.core import UnsupportedError, UserInputError
  from ax.modelbridge.base import ModelBridge
  from ax.modelbridge.cross_validation import assess_model_fit, compute_diagnostics, cross_validate
  from ax.modelbridge.generation_strategy import GenerationStrategy
  from ax.modelbridge.modelbridge_utils import observed_pareto_frontier, predicted_pareto_frontier
  from ax.modelbridge.registry import get_model_from_generator_run, ModelRegistryBase, Models
  from ax.modelbridge.torch import TorchModelBridge
  from ax.modelbridge.transforms.utils import derelativize_optimization_config_with_raw_status_quo
  from ax.plot.pareto_utils import get_tensor_converter_model
  from ax.utils.common.logger import get_logger
  from ax.utils.common.typeutils import checked_cast, not_none
  from numpy import NaN
  from torch import Tensor
label_desc: Use image node_id 15 for calling a global function with example usage: _filter_feasible_rows(df, optimization_config) and returns: feasible