code: def build_parallel_frame(
      experiments, names=None, with_evc_tree=True, order_by="objective"
  ):
      """Builds the dataframe for the parallel assessment"""
      exp_parallels = dict(
          name=list(),
          objective=list(),
          n_workers=list(),
          duration=list(),
      )
      average_key = "objective,duration"
      group = "n_workers"
      x = group
      x_title = "Number of workers"
      y = "objective"
      for i, experiment in enumerate(experiments):
          dfs = experiment.to_pandas(with_evc_tree=with_evc_tree)
          dfs = dfs.loc[dfs["status"] == "completed"]
          if len(dfs) > experiment.max_trials:
              dfs = dfs.head(experiment.max_trials)
          if not dfs.empty:
              start = dfs["suggested"].iloc[0]
              end = dfs["completed"].iloc[-1]
              duration = (end - start).seconds + 1
  
              dfs = dfs.sort_values(order_by)
              exp_parallels["duration"].append(duration)
              name = (
                  names[i]
                  if names
                  else f"{experiment.name}-v{experiment.version}"
              )
              exp_parallels["name"].append(name)
              exp_parallels[group].append(experiment.executor.n_workers)
              exp_parallels["objective"].append(
                  dfs["objective"].tolist()[0]
              )
  
      return (
          pd.DataFrame(exp_parallels),
          group,
          average_key,
          x,
          y,
          x_title,
      )
  
imports: import functools
  from collections.abc import Iterable
  import numpy
  import pandas
  import plotly.express
  import plotly.graph_objects
  from plotly.subplots import make_subplots
  import orion.analysis
  import orion.analysis.base
  from orion.algo.space import Categorical, Fidelity
  from orion.core.worker.transformer import build_required_space
label_desc: Use image node_id 6 for calling a global function with example usage: build_parallel_frame(experiments, names, with_evc_tree, order_by) and returns: pd,  group,  average_key,  x,  y,  x_title