code: def _get_single_pareto_trace(
      frontier: ParetoFrontierResults,
      CI_level: float,
      legend_label: str = "mean",
      trace_color: Tuple[int] = COLORS.STEELBLUE.value,
      show_parameterization_on_hover: bool = True,
  ) -> go.Scatter:
      primary_means = frontier.means[frontier.primary_metric]
      primary_sems = frontier.sems[frontier.primary_metric]
      secondary_means = frontier.means[frontier.secondary_metric]
      secondary_sems = frontier.sems[frontier.secondary_metric]
      absolute_metrics = frontier.absolute_metrics
      all_metrics = frontier.means.keys()
      if frontier.arm_names is None:
          arm_names = [
              f"Parameterization {i}"
              for i in range(len(frontier.param_dicts))
          ]
      else:
          arm_names = [f"Arm {name}" for name in frontier.arm_names]
  
      if CI_level is not None:
          Z = norm.ppf(1 - (1 - CI_level) / 2)
      else:
          Z = None
  
      labels = []
  
      for i, param_dict in enumerate(frontier.param_dicts):
          label = f"<b>{arm_names[i]}</b><br>"
          for metric in all_metrics:
              metric_lab = _make_label(
                  mean=frontier.means[metric][i],
                  sem=frontier.sems[metric][i],
                  name=metric,
                  is_relative=metric not in absolute_metrics,
                  Z=Z,
              )
              label += metric_lab
  
          parameterization = (
              _format_dict(param_dict, "Parameterization")
              if show_parameterization_on_hover
              else ""
          )
          label += parameterization
          labels.append(label)
      return go.Scatter(
          name=legend_label,
          legendgroup=legend_label,
          x=secondary_means,
          y=primary_means,
          error_x={
              "type": "data",
              "array": Z * np.array(secondary_sems),
              "thickness": 2,
              "color": rgba(trace_color, CI_OPACITY),
          },
          error_y={
              "type": "data",
              "array": Z * np.array(primary_sems),
              "thickness": 2,
              "color": rgba(trace_color, CI_OPACITY),
          },
          mode="markers",
          text=labels,
          hoverinfo="text",
          marker={"color": rgba(trace_color)},
      )
  
imports: import warnings
  from typing import Dict, Iterable, List, Optional, Tuple, Union
  import numpy
  import pandas
  import plotly.graph_objs
  from ax.core.experiment import Experiment
  from ax.core.objective import MultiObjective
  from ax.core.optimization_config import MultiObjectiveOptimizationConfig, OptimizationConfig
  from ax.core.outcome_constraint import ObjectiveThreshold
  from ax.exceptions.core import UserInputError
  from ax.plot.base import AxPlotConfig, AxPlotTypes, CI_OPACITY, DECIMALS
  from ax.plot.color import COLORS, DISCRETE_COLOR_SCALE, rgba
  from ax.plot.helper import _format_CI, _format_dict, extend_range
  from ax.plot.pareto_utils import ParetoFrontierResults
  from ax.service.utils.best_point_mixin import BestPointMixin
  from ax.utils.common.typeutils import checked_cast, not_none
  from plotly import express
  from scipy.stats import norm
label_desc: Use image node_id 6 for calling a global function with example usage: _get_single_pareto_trace(frontier, CI_level, legend_label, trace_color, show_parameterization_on_hover) and returns: go